Project scheduling in resource-constrained mode is one of the most important issues in the field of project management. The main philosophy of this problem is to use less resources while respecting the resource limit to complete the project in a shorter time although other goals can be considered. When a very large amount of data is generated by the meta-heuristic algorithm and there are many variables involved in solving the problem, no other algorithm or technique is able to analyze the output. For this purpose, learnheuristics have the ability to use combined metaheuristics and machine learning tools with high accuracy and in less time to analyze data. The primary purpose of this research is to combine machine learning and genetic algorithms to reduce the project completion time which can lead to a reduction in the cost of the project. Due to the population-based nature of the problem a large amount of initial population was generated. In order to convert the generated schedules into feasible ones, a repair strategy was used. A data matrix was created to import data into the ML model. After specifying the training and testing settings of the model, the decision tree was used to analyze the data of the problem, then its output was applied to the initial population using the displacement or relocation procedure. This manipulated population is given to Genetic Algorithm (GA) and continues until a certain iteration. j60data on the PSPLIB website was used to evaluate the suggested approach. The findings indicate that the implemented approach has improved by 21.75% compared to the normal GA. This improvement means that a better solution could be achieved in less time with fewer calls.