Travel time prediction is crucial for Intelligent Transportation Systems (ITS) in urban areas, particularly due to rise in vehicle numbers which causes traffic congestion, especially during peak hours. To lessen this, strategies promoting public transport usage, like delivering precise bus arrival information, are vital. This study focuses on utilizing real-time data from buses in Mysore City, specifically the BEML Nagar Route (Route 94) and the Ilavala Route (Route 260), to forecast travel times under varying traffic conditions. Employing statistical models like Autoregressive Integrated Moving Average (ARIMA) and machine learning techniques such as Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN), the research aims for accurate bus travel time prediction. Model efficacy is assessed using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). LSTM models outperform others, offering better precision for short-term predictions, while SVM models excel in representing overall transit trends. Selection between LSTM and SVM models depends on specific analysis needs, with LSTM preferred for precise short-term predictions and SVM for understanding long-term trends. By integrating statistical and machine learning methodologies, this study aims to enhance public transportation reliability and efficiency in Mysore City, contributing to reduced congestion, improved passenger satisfaction, and overall urban mobility enhancement.