Integrating Artificial Intelligence Techniques for Predictive Project Scheduling, Dynamic Resource Allocation, and Accurate Cost Estimation

Current project administration struggles with static planning methods including CPM, PERT, and EVM because they cause schedule delays and resource mismanagement with elevated costs; therefore the developed study builds an AI-powered integrated platform that enhances scheduling predictions alongside adjustable resource handling and specific cost estimations. Using a sequential explanatory mixed-methods design, researcher first conducted quantitative experiments on 200 historical projects from construction, IT, and manufacturing sectors augmented by 50 simulated “what-if” scenarios, training three AI modules—a Long Short-Term Memory (LSTM) ensemble for scheduling, a hybrid Deep Q-Network–Genetic Algorithm for resource allocation, and a neural-network cost estimator augmented with NLP-extracted risk factors—and benchmarking them against traditional methods via MAE, MAPE, R², and resource utilization metrics, with paired t-tests confirming all performance gains as statistically significant (p < .005). The analysis consisted of 18 practitioner interviews alongside focus groups to understand necessary adoption elements which included understandable explanation modules, smooth usability with other PM tools and strong data protection measures together with comprehensive training programs. The implementation of AI resulted in a 63.6% decrease of schedule deviations together with a 27.9% rise of resource utilization combined with a 72.2% reduction of cost-estimation errors leading to the potential transformation of organizational project control from reactive to proactive control. The research finds an executable guide for using AI responsibly while putting users first along with suggestions for upcoming investigations focused on adapting AI approaches to regulated sectors and creating explainable counterfactual methodology and connecting both methods to IoT and digital-twin systems and performing extended field tests to validate ROI and collaborative human-computer models.