Agriculture, as an indispensable facet of human civilization and a vital economic contributor, plays a pivotal role in sustaining societies. Bangladesh, a nation heavily reliant on agriculture, currently employs traditional methods, resulting in a significant impact on its economic stability. Improvements in agricultural technology and practices have the potential to increase productivity, reduce waste, and improve the quality of agricultural products. As the global population swells and food demand rises, sustainable agricultural methods become increasingly crucial. To address this challenge, we have developed an AI-based precision farming system employing machine learning algorithm, including CNN networks, to analyze and predict crop health and potential yield. Additionally, we propose an AI-based decision-making module that forecasts crop yields, pest detection, and disease detection. Our system, rigorously evaluated using real-world data, has demonstrated substantial improvements in crop yield, fostering sustainability and contributing to global food security while minimizing environmental impact.