This study investigates Green AI and Sustainable AI literature through a dual analytical approach, combining thematic analysis with BERTopic modeling to identify broad thematic clusters and emerging topics. Three major thematic clusters were identified: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, emphasizing energy efficiency; and (3) Big Data-Driven Computational Advances, highlighting AI's influence on socio-economic and environmental aspects. BERTopic modeling uncovered five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals intersections between Sustainable and Ethical AI and Green Computing, identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI's socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. The study provides insights for future research directions and policy interventions. The research methodology combines Scientometrics and BERTopic analyses to comprehensively map and analyze the existing body of literature, offering a nuanced understanding of the current landscape and emerging trends in the application of AI for sustainable and socially impactful outcomes. The study identifies three thematic clusters and five emerging topics, highlighting the importance of ethical and environmental considerations in AI development. The findings contribute to the understanding of AI's role in sustainability, ethical considerations, and the development of sustainable AI practices. The study also discusses the potential of AI in various domains, including healthcare, energy optimization, and educational applications, emphasizing the need for responsible and ethical AI development. The research highlights the importance of interdisciplinary collaboration and the need for policies that ensure the sustainability and ethical use of AI technologies. The study provides a comprehensive analysis of the current state of Green and Sustainable AI research, identifying key areas of focus and emerging trends. The findings underscore the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research and policy interventions.This study investigates Green AI and Sustainable AI literature through a dual analytical approach, combining thematic analysis with BERTopic modeling to identify broad thematic clusters and emerging topics. Three major thematic clusters were identified: (1) Responsible AI for Sustainable Development, focusing on integrating sustainability and ethics within AI technologies; (2) Advancements in Green AI for Energy Optimization, emphasizing energy efficiency; and (3) Big Data-Driven Computational Advances, highlighting AI's influence on socio-economic and environmental aspects. BERTopic modeling uncovered five emerging topics: Ethical Eco-Intelligence, Sustainable Neural Computing, Ethical Healthcare Intelligence, AI Learning Quest, and Cognitive AI Innovation, indicating a trend toward embedding ethical and sustainability considerations into AI research. The study reveals intersections between Sustainable and Ethical AI and Green Computing, identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI's socio-economic and societal impacts. The study advocates for a unified approach to innovation in AI, promoting environmental sustainability and ethical integrity to foster responsible AI development. This aligns with the Sustainable Development Goals, emphasizing the need for ecological balance, societal welfare, and responsible innovation. The study provides insights for future research directions and policy interventions. The research methodology combines Scientometrics and BERTopic analyses to comprehensively map and analyze the existing body of literature, offering a nuanced understanding of the current landscape and emerging trends in the application of AI for sustainable and socially impactful outcomes. The study identifies three thematic clusters and five emerging topics, highlighting the importance of ethical and environmental considerations in AI development. The findings contribute to the understanding of AI's role in sustainability, ethical considerations, and the development of sustainable AI practices. The study also discusses the potential of AI in various domains, including healthcare, energy optimization, and educational applications, emphasizing the need for responsible and ethical AI development. The research highlights the importance of interdisciplinary collaboration and the need for policies that ensure the sustainability and ethical use of AI technologies. The study provides a comprehensive analysis of the current state of Green and Sustainable AI research, identifying key areas of focus and emerging trends. The findings underscore the critical need for integrating ethical and environmental considerations into the AI development lifecycle, offering insights for future research and policy interventions.