The exponential growth of sports data has created a compelling opportunity to extract actionable insights through interactive analytical platforms. This paper presents the design and implementation of a web-based Olympic data analysis system developed using Python and Streamlit. The proposed system processes a comprehensive historical dataset spanning 120 years of Olympic competition, encompassing more than 271,000 athlete-event records. Analytical modules are developed to examine medal tallies, country-wise performance trajectories, athlete biometric distributions, sport-wise participation patterns, and longitudinal gender participation trends. Data preprocessing employs Pandas-based pipelines to address missing values, eliminate duplicate records, and engineer derived features. Visualization is achieved through a multi-library strategy utilizing Matplotlib for static charts, Seaborn for statistical graphics, and Plotly for fully interactive, user-driven figures. The Streamlit framework provides a reactive web interface enabling real-time filtering without requiring server-side scripting expertise. Benchmark measurements indicate an average dashboard load time of 1.8 seconds and a full-dataset processing time of 0.42 seconds. The study identifies limitations in real-time data integration and proposes future extensions including machine-learning-based medal prediction and cloud deployment.