The rapid developments in the field of machine learning (ML) have introduced new possibilities for addressing the complex dynamics involved in train–track–bridge (TTB) interactions. Traditional approaches in dynamic analysis of railway bridges are computationally intensive and require significant expertise. This study investigates the application of ML algorithms to predict the dynamic acceleration response of railway bridges under high-speed train loads. A comprehensive dataset of 800,000 randomly generated bridge configurations was utilized to train and validate the models. The regression analysis performed with moderate accuracy and was unable to fully capture the nonlinear interactions among variables. To address this limitation, a Random Forest model was implemented, significantly enhancing prediction accuracy. The model's performance indicates its high reliability and suitability for practical applications in structural engineering, particularly in the preliminary assessment of railway bridges. This research contributes to the application of predictive modeling in railway bridge engineering, laying the foundation work for studying the complex dynamics involved in TTB modeling.