This integrates the Internet of Things and artificial intelligence technologies to optimize agricultural productivity while minimizing environmental impact. The critical challenge of enhancing agricultural productivity while reducing resource consumption and environmental degradation is faced by agricultural systems under greater pressure to maintain food security. Analyzed the effectiveness of irrigation, fertilization, and pest management using sensor networks integrated with AI algorithms for smart farming at 47 different geographical locations through a comprehensive study. Results revealed that IoT-AI integration reduced water usage by 28%, fertilizer consumption by 32%, and crop yield by 24% in comparison with traditional methods of farming. The machine learning model trained using data generated through the IoT delivered up to 91% precision on the task of optimal resource allocation and even precocious early stress detection for the crops. These results strongly support the contention that IoT-powered AI-based interventions may play an extremely crucial role in agricultural sustainability as they open possibilities for appropriate, time-aware, and sustainable management of crop growth resources as well as efficient remedial intervention to address environmental imbalance.