The objective of this paper is to develop a model using Support Vector Machine that may predict weather factors such as temperature, humidity, precipitation, and wind speed, which normally affect construction project timelines. In this study, actual progress data of the projects and meteorological data obtained from meteorological stations and online weather services are utilized. The independent variables involve preprocessed and normalized weather factors, which enter the model. The data is divided into two parts: training and testing, in order to assess the performance of the model. The SVM model with the RBF kernel was adopted because it can handle nonlinear data and perform better in predicting the changes in construction progress that may be caused by different weather conditions. The results of the study show that the model is highly accurate, with performance metrics such as MSE, RMSE, and R2 yielding good results, demonstrating the model’s ability to predict construction delays. This research is very important in helping construction managers and engineers in project planning and timeline adjustments that help optimize resources and minimize costs incurred due to weather-related delays.