The accurate prediction of travel time in India is essential for efficient transportation planning and management, especially in busy cities with heavy traffic. Recent research in transportation engineering, computer science, and data analytics has demonstrated the effectiveness of data-driven approaches in predicting travel times more accurately. Accurate road travel time prediction can benefit both transportation service providers and travelers by improving trip planning, reducing travel times, and increasing operational efficiency. Driven by a vision to make a significant contribution to this expansive realm of research, this paper suggests a methodology for forecasting taxi travel times for Bangalore city using data from Uber Movement to increase the effectiveness and dependability of urban transport networks. The model combines regression approaches with spatiotemporal data analysis to capture complicated geographical and temporal trends. The model adds pertinent data, such as weather from Wunderground, and is trained using Uber Movement’s data on cab trips. This report provides a thorough trend analysis of Bangalore city’s commuters. With a Mean Absolute Error (MAE) of 103 s on the Uber data, the ExtraTree regressor outperforms the other regression methods, according to a comparison of their findings. Based on Uber Movement data, the suggested algorithm can accurately predict the time needed to travel between places in Bangalore city. Furthermore, a comprehensive evaluation is undertaken to analyze the suitability of eXplainable Artificial Intelligence (XAI) in effectively addressing the interpretability of black box machine learning models for predicting travel time. The XAI methods, SHAP and LIME are used to evaluate the interpretability of the trained models. As part of future work, this model can be applied to other cities and improve traffic management by taking proactive measures in advance.