This paper presents an intelligent, data-driven Crop Advisory System designed to enhance the resilience andsustainability of modern agriculture. The system addresses the critical challenge of optimal crop selection byleveraging a Random Forest Classifier trained on a comprehensive dataset of agronomic and environmentalparameters. The model achieved a predictive accuracy of approximately 99.3% and is deployed as an interactiveweb application using the Flask framework. A key innovation is the system’s ability to provide a diversified listof the top three most suitable crops, mitigating the economic risks associated with market saturation frommonoculture trends. The platform integrates a live weather API for real-time data accuracy and generates a multifaceted analytical dashboard with dynamic visualizations to support farmer decision-making. By translatingcomplex data into accessible and actionable insights, the system directly contributes to the principles ofSustainable Development Goal 2 (SDG 2), promoting efficient resource management, improving food security,and strengthening the economic viability of farmers.