Agricultural crop yield prediction is crucial for enhancing food security, optimizing resource use, and ensuring sustainable agricultural practices. This project focuses on enhancing food security and sustainable agricultural practices by predicting crop yields using machine learning techniques. This project leverages advanced data analysis techniques, including machine learning and statistical models, to accurately forecast crop yields. The study investigates the correlation between crop yield and crucial input variables such as nitrogen, phosphorous, potassium, rainfall, temperature, and fertilizer application. The primary objective is to develop accurate and reliable predictive models that enable farmers and agricultural stakeholders to anticipate crop yields, aiding in better planning and resource allocation. The research examines the correlation between crop yield and environmental factors such as nitrogen, phosphorus, potassium, rainfall, temperature, and fertilizer application. Multilinear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM) models are applied to predict yields, with SVM achieving the highest accuracy at 92.03%, followed by MLR at 88.56% and RBF at 75.36%. The data collection for this study includes pesticides usage, historical weather parameters, and fertilizer usage from the Peddapalli district, Telangana, India. MLR identifies linear relationships, RBF captures non-linear patterns, and SVM handles high-dimensional data to enhance prediction accuracy. The results indicate that while MLR and RBF provide valuable insights, SVM is the most robust tool for forecasting crop yields. This research holds significant potential for improving agricultural productivity and resource management, offering farmers crucial insights for better planning and allocation of resources.