Accurate crop yield prediction is a significant challenge due to the variability in climatic conditions and soil fertility. Traditional models often fail to account for these dynamic factors, leading to suboptimal predictions and inefficiencies in agricultural planning. This paper addresses the specific problem of incorporating real-time environmental data into crop yield prediction models. Our proposed solution integrates Internet of Things (IoT) devices with machine learning algorithms on a cloud-based platform to continuously monitor and analyze a wide range of environmental conditions, including temperature, humidity, soil moisture, and nutrient levels. By leveraging real-time data collection and advanced cloud analytics, our system dynamically adjusts its predictions to reflect current conditions, thereby enhancing the precision of yield forecasts. The cloud-based infrastructure ensures scalability and accessibility, allowing farmers to receive timely and accurate information regardless of their location. This approach not only provides farmers with reliable forecasts but also supports adaptive agricultural practices, enabling proactive decision-making and resource optimization. The implementation of this system demonstrates significant improvements in prediction accuracy and agricultural efficiency, leading to better crop performance and sustainability. Our approach equips farmers with the knowledge and skills they need to adapt to changing environmental circumstances and succeed in today’s complicated agricultural landscape.