Driver dwell time is an important challenge the U.S trucking industry faces. High, unplanned dwell times are costly to all stakeholders in the industry as they result in detention costs, declining performance and decreased driver capacity. With the increasing demand for these services, it is important to maximize the driving time of drivers in the industry by minimizing dwell time to free up capacity and provide competitive wages. This project utilizes the data of a third-party logistics company with the goal to understand the factors that influence dwell time, and to construct the model to predict dwell time of a load. In the analysis, linear models, random forest, and gradient boosting methods were explored based on regression and classification approach. Ultimately, the random forest classification model with one-hour bins is the recommended model as it had the highest predictive performance while the one-hour bins was sufficient to meet the business need. Additionally, the analysis concludes that shipper facilities are the most significant driver of dwell time. Hence, understanding and integrating more granular observations on shipper practices within their facilities will allow a third-party logistics company to improve its driver fleet utilization and increase the predictive performance of their dwell time prediction model.