A primary task to a project manager is to ensure that the project proceeds timely and on the budget. Earned value management (EVM) is the most common method to evaluate and further predict a project in terms of time and cost. Because of poor accuracy in forecasting different durations, the application of the EVM for schedule performance prediction has been limited. Therefore, in this paper, the Kalman filter was used to analyze the data and compare the results with those of a few hybrid machine learning (HML) techniques. The innovation of this study comes in the face of the prediction of the project time and cost by efficient machine learning algorithms based on some directly measurable variables. For this purpose, 398 data points from five different dam projects were used to predict two output variables by hybrid schemes of multilayer perceptron (MLP) algorithm combined with the genetic algorithm (GA) and particle swarm optimization (PSO), herein referred to as MLP-GA and MLP-PSO, respectively. Four input variables, namely earned schedule (month), earned value ($), actual progress (%), and actual cost (%), were considered for time prediction, and the time (month) was considered as an input variable for cost prediction. The results showed that early warnings for the risk of delay in project schedule and cost were generated in months 1 and 8 by the MLP-PSO, months 1 and 11 by the MLP-GA, and rather months 7 and NV (no value) for the Kalman filter, indicating the fast operation coupled with high accuracy of the HML algorithms, as compared to the Kalman filter. In addition, the two HML algorithms were compared based on statistical error indices to determine the best one. After reviewing the results, the MLP-PSO was found to outperform the MLP-GA in terms of convergence rate and accuracy. To the best of our knowledge, none of the previous studies have used the proposed hybrid algorithms (MELM-PSO and MELM-GA) for forecasting, simultaneously, the time and cost as two critical parameters in the project management, indicating the novelty of our research.