Artificial intelligence (AI) is rapidly becoming a game-changing tool for tackling global environmental issues. The purpose of this research is to explore how Artificial Intelligence can be applied to drive advance sustainability enterprise across diverse sectors. For instance, numerous associations are formerly tapping into AI technologies to enhance energy effectiveness. By incorporating AI into sustainability systems and processes, associations can optimize resource application, reduce waste, and save energy and capitalist. An illustration of this is smart grids, where AI-powered algorithms can play a transformative part in revolutionizing energy operation. The methodologies for accelerating sustainability with AI involve relating and assaying sustainability challenges, developing AI results, enforcing AI results, monitoring and assessing issues, conforming and perfecting. AI is reshaping sustainability attempts by allowing associations to minimize operations, reduce waste and accelerate the adoption of low-carbon technologies. By integrating AI into sustainability initiatives, companies can improve efficiency and foster new business models that align environmental responsibility with economic growth. An association between AI and sustainability is not only perfecting effectiveness but also creating new openings for invention. From energy operation to agriculture and climate monitoring, AI is proving to be an important tool in the fight against environmental challenges. As we look to the future, it is clear that AI will play a vital part in creating a more sustainable and adaptable world.

Social media platforms have become critical sources of real-time information for monitoring and predicting civil unrest and violent events. Recent advances in artificial intelligence have produced a wide range of analytical pipelines, including transformer-based language models, graph neural networks, temporal forecasting systems, and multimodal vision–language frameworks. However, existing studies remain fragmented across platforms, languages, and modeling paradigms, making it difficult to assess their relative effectiveness and applicability. This paper presents a comprehensive comparative review of AI-based architectures used for civil unrest prediction using Twitter and Instagram data. The study systematically analyzes model categories, data representations, performance trends, platform suitability, multilingual capability, and ethical considerations. By synthesizing findings across recent literature, this work highlights architectural trade-offs, identifies persistent research gaps, and provides practical guidance for selecting appropriate analytical frameworks. The review contributes toward a clearer understanding of current methodological capabilities and limitations in socially responsible unrest prediction.

The preservation of global cultural heritage is facing an unprecedented crisis. Rapid urbanization, geopolitical conflict, and environmental degradation are erasing historical sites at an alarming rate, often leaving behind only fragmented ruins or digital photographs. Current digital preservation methodologies, such as Photogrammetry and LiDAR (Light Detection and Ranging), have enabled the creation of high-fidelity "Digital Twins." However, these methods are fundamentally limited; they capture the site only as a static, frozen artefact, devoid of the complex temporal and social contexts that shaped it. They present history as a finished product rather than an evolving process. This research proposes a novel AI-Enhanced Reconstruction Framework that transcends traditional documentation by integrating Natural Language Processing (NLP), Neural Radiance Fields (NeRF), and Generative Adversarial Networks (GANs). We introduce the concept of "Branching Historical Timelines," a computational approach that allows users to explore not just the history that occurred, but also "counterfactual" or alternative histories based on variable pivot points. By integrating these generative models into an immersive Virtual Reality (VR) environment, the proposed system transforms passive observation into active historical inquiry. This framework offers a robust tool for both educational simulation and digital humanities research, shifting the paradigm from static preservation to dynamic simulation.

As judicial backlogs reach critical levels globally, the integration of Artificial Intelligence (AI) has transitioned from administrative support to "predictive justice." Artificial Intelligence (AI) is transforming the judicial ecosystem worldwide — from automating legal research to assisting in bail and sentencing decisions. The concept of Predictive Justice, powered by data analytics and machine learning, aims to make legal outcomes more efficient and consistent. However, the introduction of “Robo-Judges”- autonomous or semi-autonomous and predictive algorithms raises profound ethical and legal confusions: Can justice truly be fair when delivered by machines? This paper explores the applications of AI in justice delivery, the potential benefits, and the serious risks of bias, opacity, and moral detachment. It argues that while AI can support judges and streamline processes, human judgment must remain the centre of Trails to ensure fairness, empathy, and accountability in the rule of law.

The rapid advancement of Artificial Intelligence (AI) has significantly transformed corporate governance and compliance monitoring frameworks across organizations. In the context of increasing regulatory complexity and strict enforcement of corporate tax laws, AI has emerged as a strategic tool for enhancing accuracy, transparency, and efficiency in tax compliance processes. This study examines the role of Artificial Intelligence in corporate governance and compliance monitoring with specific reference to corporate tax compliance in the Thane region. The research adopts a descriptive research design and utilizes both primary and secondary sources of data. Primary data were collected through a structured questionnaire using a five-point Likert scale, while secondary data were sourced from books, journals, research articles, and regulatory publications. Reliability analysis was conducted to test the consistency of the scale, followed by one-sample t-tests for data analysis. The study incorporates key behavioral and competency variables such as frequency of AI usage, risk tolerance, digital literacy, motivation, consistency, herding behaviour, and financial literacy. The findings indicate that Artificial Intelligence plays a significant role in strengthening corporate governance practices and improving corporate tax compliance by enabling continuous monitoring, reducing human errors, and ensuring regulatory adherence. The study provides valuable insights for corporate decision-makers, tax professionals, and policymakers seeking to leverage AI for effective governance and compliance management.

Artificial Intelligence is no longer just a technological tool it is becoming a part of how we learn, connect, and experience society. This paper explores the role of AI in Education with a focus on how it shapes ethical and inclusive development, supports learning innovation, and influences everyday human behaviour. As classrooms adopt smart tutoring systems. AI technology is quietly transforming how people access knowledge and participate in community life. The study uses surveys to understand how students, teachers, and communities interact with AI-driven systems. Survey reveals that AI encourages inclusive education, helps bridge learning gaps, and enhances engagement. However, issues related to governance, justice, bias and responsible use of AI remain major, especially in diverse societies. With that, AI can contribute meaningfully to education if introduced with transparency, fairness and human-centred values.

The fast-paced development in the realm of Artificial Intelligence (AI) has appeared as a revolutionary element in the modern labor market, changing the characteristics and sustainability of job structures all over the world. Although AI-based automation provides benefits regarding productivity, innovation, and economic development, it also generates substantial concerns regarding job replacement, skill degradation, and job sustainability worldwide to a great extent. This research paper attempts to explore the idea of sustainable jobs in the AI era based on the impacts produced by AI that transform and extend job structures. This research aims to: (i) conceptualize sustainable employment in an AI-related economy, (ii) explore employment that could be at risk of automation and job opportunities, and (iii) analyze the policy direction for sustainable employment outcomes.