Recent advancements in AI have enabled more accurate land use mapping through satellite imagery, offering insights into agricultural productivity and environmental conservation. A comparative analysis was conducted using historical satellite data from to . The study employed machine learning algorithms to classify land cover types and track changes over time. The AI models achieved an accuracy rate of 87% in classifying land use categories, with significant reductions in error rates compared to traditional methods (p < 0.05). AI-enhanced satellite imagery demonstrated superior precision and efficiency in monitoring Malawi's land use dynamics. Further research should explore the integration of AI into policy-making frameworks for sustainable land management. AI, Satellite Imagery, Land Use Monitoring, Machine Learning, Agricultural Productivity Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.