Food security is one of the most essential requirements of all human life. Understanding agricultural trends is important for effective resource management, with a growing global population and the shocks of climate change impacting humanity. In this study, we introduce an interactive web application intended to enable the analysis of agricultural data for users from farmers to policymakers with tools to visualize trends and forecast crop yields at future times. The application, which was designed using the Streamlit library, takes the complexity out of data analysis and enables individuals who do not possess advanced technical skills to use it. Users can upload their datasets (CSV) and visualize some key metrics Users are able to make scatter plots to check the correlation between annual rainfall with crop yield and line charts to see how yield has progressed over the years. This allows users to see the actual evidence and use that to draw conclusions. In addition, box plots and pair plots are visualizations that give you a deeper look at yield insulation distributions and correlations between different agricultural processes. This application employs machine learning models to predictive analytics to make forecasts of future yields. It helps users understand how various factors impact crop yield, but also allow them to predict future outcomes based on past performance. With the integration of machine learning, the application helps to turn data analysis from a retrospective exercise to a proactive looking strategy.