Agriculture plays a pivotal role in global food security and economic sustainability. To meet the increasing demand for food, it is essential to maximize agricultural productivity while conserving resources and mitigating the environmental impact. The convergence of the Internet of Things (IoT), predictive analytics, and machine learning has ushered in a transformative era known as precision agriculture. This interdisciplinary approach leverages data from IoT sensors and satellites, applies predictive analytics, and harnesses the power of machine learning algorithms to optimize farming practices. Precision agriculture offers a myriad of benefits. It enables resource optimization by providing real-time data on soil conditions, weather patterns, and crop health. This data-driven approach minimizes waste, enhances yield, and reduces the need for pesticides and fertilizers. Furthermore, ML models provide predictive insights, facilitating early disease detection, precise pest management, and accurate yield forecasting. These advancements empower farmers to make informed, data-driven decisions, thereby promoting more efficient and sustainable farming practices. The future of agriculture with IoT, predictive analytics, and machine learning. Ongoing research focuses on advanced sensors, edge computing, and data integration. Additionally, ethics, data privacy, and regulatory considerations are essential areas of exploration. As technology continues to advance, it is evident that precision agriculture will play a pivotal role in addressing the food security and sustainability challenges of the 21st century. With the use of experimental data gathered from a maize farm, the suggested strategy is assessed. The findings demonstrate that the suggested method has a 95% accuracy rate for predicting crop yields. By decreasing the negative effects of farming techniques on the environment and boosting crop output, this research may help promote sustainable agriculture.