Accurately estimating the project schedule is crucial for the success of a project. Once the project is in progress, there are numerous potential disruptions that can jeopardize the workflow. If the initial forecast is significantly inaccurate, it could lead to project cancellation or incomplete deliverables due to missed deadlines. This study aims to employ various regression analysis methods to provide project managers with the ability to accurately forecast project program development hours, serving as a decision support tool. Data quality and availability also play a pivotal role, as incomplete or inaccurate data can affect the training and evaluation of prediction models. The study collected historical program development data from Company D, subjected it to preprocessing and feature selection, and employed 13 commonly used regression analysis methods for model training and evaluation. By comparing the performance of different analytical approaches, project managers can assess and select the most appropriate method for hour estimation based on project context, resource requirements, and execution efficiency. Additionally, non-expert project managers can quickly evaluate hours and understand critical factors in assessments. Therefore, this research provides valuable insights for project programming hour predictions, positively impacting project efficiency and effectiveness.