Traffic counts are essential for assessing road capacity to provide efficient, effective, and safe mobility. However, current methods for generating models for traffic count studies are often limited in their accuracy and applicability, which can lead to incorrect or imprecise estimates of traffic volume. This study focused on analyzing and predicting traffic conditions on Ikorodu Road in Lagos State. The analysis involved an examination of historical traffic data, specifically focusing on daily and hourly traffic volumes. The prediction involved the use of machine learning models, including decision trees, gradient boosting, and random forest classifiers. The results of this study revealed significant variations in traffic volume across different days of the week and times of the day, indicating peak and off-peak periods. The study also highlighted the need for a more comprehensive approach that includes additional factors, such as weather conditions, road work, and special events, which could significantly impact traffic volume.