The ability to predict the time and cost of project implementation has become one of the most important issues in the field of project management over the years. Forecasting takes place before the project is completed and at the time of the contract. In fact, based on past information about similar projects, the time and budget of the project is determined. The other part of the forecast happens during project execution. In fact, based on project progress and deviation from planned progre It is possible to predict the actual time and cost of completion of the work with more accuracy and better quality. In this dissertation, statistical, intelligent and intelligent statisticalhybrid models will be implemented to improve the accuracy of predicting the project completion time before the start of the project