The quality of the projects constructed would be analyzed by the Random Forest machine learning model with respect to parameters like the quality of the building material, construction technique, experience of the construction team, weather conditions, project budget, and the adherence to building regulations. This will involve the processing of data from 132 actual projects and the application to a random forest for the estimation of construction quality. Results indicate that among those factors that have great bearing on the quality of construction, the quality of materials and weather conditions take priority, while other aspects like budget or adherence to regulatory measures are way lower. The Random Forest model yields an accurate prediction capability and promises good results based on evaluation metrics, such as MSE and MAE. In this regard, several practical implications have been addressed that are helpful for construction project managers to make appropriate decisions with a view to enhancing quality and effectiveness in construction projects. In general, this model can be widely applied to the analysis and prediction of construction quality within the construction industry.