This paper presents a comprehensive machine learning-driven solution to improve agricultural practices by addressing key challenges such as crop yield prediction, supply chain management, and multilingual farmer communication. A neural network model achieved an accuracy of 87.6% in predicting optimal crops based on environmental and soil data, enabling informed planting decisions. Logistic regression, with an accuracy of 73.4%, was used for storage optimization, while ARIMA models improved transportation forecasting, thus streamlining supply chain operations. A standout feature of the paper is the integration of the ChatGPT API, which provides real-time multilingual support, enabling farmers to interact with the platform in their preferred languages. This significantly improves accessibility and ease of use, making advanced agricultural technology more inclusive for farmers across different regions. This solution addresses the challenges of fragmented technology platforms and accessibility issues due to language barriers. The paper provides a comprehensive, scalable system designed to boost agricultural efficiency, improve productivity, and promote sustainable farming practices, by integrating crop prediction, harvesting optimization, and supply chain management into one system.