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Predicting truck production in construction projects is one of the basic tasks within project planning and control. This paper presents an original and novel intelligent stochastic agent-based model for predicting truck production at construction sites through considering the impact of learning. The proposed model is developed to overcome limitations of existing models, including a lack of the inclusion of a training mechanism and a reward/penalty framework for truck performance. Ideas of reinforcement learning theory are used. A reward/penalty function is designed based on minimum travel time. Traffic and fuel volume are treated as stochastic variables. A worked example and a real case study are presented to show the applicability and efficiency of the proposed model. The paper shows that the results of the proposed model accurately predict truck production. The paper also shows that the proposed model demonstrates a shorter truck travel time and, thus higher production compared to the Monte Carlo simulation logic. The method proposed here offers an original contribution to the analysis of truck production, and will be of use to practitioners engaged in project planning and control, especially in large earth-moving operations.