Growing realization of the need for sustainable agriculture has seen Artificial Intelligence (AI) applications implemented in precision agriculture, advocating for sustainable use of resources and enhanced yields in crops. The authors present an AI-driven model that employs multi-modal data of soil attributes, weather, and vegetation indices to predict crop yield. It develops a model with satisfactory test prediction accuracy based on starting from a Linear Regression model (MSE of 0.0835 and R2 score of 0.9714). This shows that the model can potentially capture linear trends for big meteorological change influence on crop yields. Feature correlation analysis determines NDVI and soil moisture as the most significant predictors, with the important roles of monitoring vegetation health and effective water management yielding better agricultural results. Although Linear Regression is a robust starting point, this research also paves the way for integrating sophisticated AI techniques like Neural Networks and Ensemble Learning models to tackle non-linear relationships and interactions. In comparative analysis, we have confirmed the utility of interpretable models for scalability, reliability and applicability in smallholder farming. Implications: The result fills the void between the abstract AI structures and the usable application, providing a widely replicable artifact to improve decision-making in precision agriculture. In addition to prediction accuracy, the study also emphasizes on scalability and ethical consideration, the latter coupled with the area of application of IOT based AI empowered systems and data privacy. Further studies will seek to evaluate the model in different agricultural regions, explore new machine learning methods, and implement real time decision support systems. This study contributes to global efforts towards achieving sustainable agriculture, ensuring food security, and limiting the environmental impact of agriculture by providing knowledge that can be used directly.