Efficient public transportation systems are critical for urban mobility, requiring precise bus arrival time predictions to enhance service quality and passenger satisfaction. This study thoroughly investigates predictive modeling utilizing machine learning methodologies to forecast bus arrival times along a specific route in Phnom Penh, Cambodia. The dataset contains historical bus arrival data from a specific route, including journey duration, hour of day, day of the week, distance, speed, current bus stop, next bus stop, and weather conditions. The predictive models of this research are built using machine learning methods such as linear regression, XGBoost, support vector machine (SVM), k-nearest neighbors (KNN), and artificial neural network (ANN). These algorithms are used to create prediction models, which are fine-tuned to yield the most accurate estimates of bus arrival times. The usefulness of these models is systematically tested using common performance metrics such as the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), offering a full assessment of predicting. Extensive experimentation data shows the best performance of XGBoost model, which consistently outperforms other machine learning methods in accurately estimating bus arrival times, with an MAE of 16 seconds, an RMSE of 28.03 seconds, and a MAPE of 2.61%. Our findings contribute to the development of predictive modeling tools in urban transportation planning and management. This study provides a vital tool for improving the performance and sustainability of Phnom Penh’s public transit systems by using the power of machine learning and considering a wide range of parameters, including those not covered by traditional datasets.