Predicting vehicle sales in the Mexican market is crucial due to its complex and multifactorial nature. This study leverages advanced machine learning models to predict sales of passenger trucks and electric vehicles. Our primary contribution is developing a robust model using historical data and various influencing factors, evaluated by metrics such as mean squared error and root mean squared error to ensure accuracy and reliability. The results demonstrate the effectiveness of these models in forecasting vehicle sales, providing valuable insights for the automotive industry. Accurate sales predictions enable stakeholders to make informed decisions, manage supply chains, allocate resources efficiently, and tailor marketing efforts. As electric vehicles gain traction and the passenger truck market evolves, our findings contribute significantly to the industry by enhancing decision-making processes and fostering data-informed strategies.