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This research study aims to explore the potential benefits of integrating machine learning in the LPS by developing a framework to identify constraints affecting the project. The study seeks to evaluate the accuracy and effectiveness of machine learning models in predicting and managing lookahead constraints of construction projects by analyzing structured and unstructured data from LPS schedules and constraint analysis. The study's findings can help construction companies to streamline workflows, increase efficiency, reduce errors, and improve the overall quality of construction projects.