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The growing global population and the increasing demand for food have led to a pressing need for sustainable agricultural practices. To address this challenge, we present an AI-Based Precision and Intelligent Farming System that leverages state-of-the-art machine learning techniques to optimize resource utilization and crop yields. This study demonstrates the integration of various data sources such as satellite imagery, IoT sensors, and historical data to develop a comprehensive and adaptive system for precision agriculture. Our approach employs deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to analyze and predict crop health, growth, and potential yield. Furthermore, we propose a reinforcement learning-based decision-making module for effective irrigation, fertilization, and pest control management. The proposed system is extensively evaluated on real-world datasets, showing significant improvements in crop yield, water efficiency, and overall sustainability compared to traditional farming methods. Our findings suggest that the AI-Based Precision and Intelligent Farming System has the potential to revolutionize agriculture and contribute to global food security while minimizing environmental impacts.