Urban environments face complex and multifaceted challenges that demand innovative solutions for sustainable development. This research ex-plores the potential of artificial intelligence (AI) in optimizing urban envi-ronments through a comprehensive, AI-driven approach to environmental stewardship. By evaluating the efficacy of AI applications across seven key areas like as pollution management, waste management, energy optimiza-tion, water resource management, transportation systems, biodiversity con-servation, and disaster management. This study identifies significant im-provements and the challenges associated with each domain. Our proposed algorithm systematically processes and analyzes diverse data sources, inte-grates technical, ethical, and social considerations, and synthesizes findings into actionable insights. The results, presented through a numeric data com-parison, demonstrate substantial effectiveness of AI applications, with nota-ble improvements in pollution reduction (85%), waste collection efficiency (80%), energy savings (75%), water waste reduction (78%), traffic conges-tion decrease (82%), biodiversity metrics increase (70%), and disaster re-sponse time enhancement (88%). Despite these successes, challenges such as data quality, infrastructure costs, and algorithmic bias persist. The study highlights the critical need for enhanced data integration, ethical AI practic-es, and interdisciplinary collaboration to fully realize the potential of AI in urban environmental management. Future research should focus on address-ing these challenges and exploring new AI-driven solutions to foster sustain-able, efficient, and resilient urban environments.