The construction industry is increasingly confronted by the uncertainty of megaprojects, site variation, and the need to deliver low cost and on time. Traditional approaches to scheduling and resource management such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) cannot account for uncertainty, maintain pace with in-progress changes, or benefit from growth in digital data availability. This article proposes an integrated framework that brings together machine learning (ML) techniques and engineering management practices to realign construction scheduling and resource allocation to optimize. Supervised models and deep learning models are utilized to forecast task durations and delay risks from historical project data, sensor reading data, and Building Information Modelling (BIM)–indexed datasets. Clustering methods are utilized to cluster tasks and resources in order to minimize the complexity of scheduling, and reinforcement learning (RL) agents adaptively modify allocations with uncertainty. For providing feasibility and satisfaction of engineering constraints, RL outputs are verified by mixed-integer linear programming (MILP). The approach is tested with simulated data and case studies and achieves significant improvement in project completion time, resource allocation, and tolerance to delay compared to conventional methods. The findings countenances that ML-based decision-making has immense potential in transforming construction management practice.