The rapid increase in transportation vehicles in urban areas has led to several challenges, such as long traffic queues, increased carbon emissions, and other environmental and infrastructural issues. To address these problems, governments encourage citizens to use public transportation services instead of private vehicles. However, to rely effectively on public transport, individuals need to be aware of transit schedules as well as potential delays caused by the dynamic nature of smart cities and changing weather conditions. Conventional transit tracking systems mainly emphasize real-time monitoring and offer limited predictive insights into service reliability. To overcome this limitation, the proposed intelligent public transportation analytics system features a Flask-based backend integrated with machine learning models that analyze historical data, traffic density (using the TomTom API), and weather patterns to predict potential delays. The processed data is stored and displayed through a React-based frontend dashboard, allowing users to interactively view performance metrics, real-time updates, and predictive analytics. The system provides insights for commuters and authorities by identifying factors like weather, peak-hour congestion, and special events. By integrating multiple data sources and predictive models, it enhances transit reliability, optimizes route planning, and supports data-driven urban traffic management.