It will use ANN in order to analyze and optimize resource usage on construction projects, hence reducing waste and generally improving a project's overall efficiency. Machine learning methods are adopted in this work for the development of an ANN model which could be used for prediction and analysis of resource use effectiveness in terms of type of project, scale of resources, level of automation, and influence of project schedule. A total of 151 real-world construction projects were collected and preprocessed to serve as data for the model. Models are trained with different performance metrics they will have to meet: MSE, MAE, RMSE, and R2 assessment of the accuracy of the ANN model. Fully connected ANNs can be used to effectively predict resource usage effectiveness with a high degree of accuracy in resource optimization and waste minimization. Therefore, this research will be important and provide a powerful tool contributing to the derivation of assistance for construction managers in making more precise decisions, optimizing resources, and increasing economic efficiency at construction projects.