One type of transportation system developed in several cities is the Bus Rapid Transit (BRT) system. BRT systems are influenced by various factors, and route planning is one of the most important ones, which involves aspects such as route design, bus schedules, and passenger load. BRT systems can generate certain service data, which can be useful for calculating passenger load. However, these service data are insufficient to accurately predict future passenger loads. Processes such as origin–destination matrix analysis are required, which are time-consuming and not suitable in most cases. This paper proposes a machine learning (ML) model that allows predicting passenger load at the key stations of a BRT system. An exploration of datasets from several BRT systems was performed for a particular use case. Open data on the Transmilenio BRT system from Bogotá (Colombia) was determined as the source. The obtained results showed that the model using the Long-Short Term Memory (LSTM) algorithm obtained the best results in the metrics using one of the two generated datasets. However, the initial results were not satisfactory enough, so it was necessary to use a hyperparameter-tuning tool and vary the range of dates in the dataset to improve the respective metrics.