Artificial Intelligence for Prediction of Cost and Schedule Performance in Nigerian Construction Projects: A Systematic Review and Future Directions

Artificial intelligence is increasingly influencing construction project management by strengthening the prediction of cost and schedule performance, two of the most important indicators of project success. This systematic review examined recent peer reviewed literature on the application of artificial intelligence in construction forecasting, with emphasis on cost overrun, time overrun, project delay, and the practical implications for Nigerian construction projects. Guided by PRISMA 2020, the review synthesized evidence published between 2020 and 2026 and screened 1,042 records, of which 35 studies were retained for the final review. The evidence shows that artificial intelligence has advanced from basic neural network approaches to more sophisticated ensemble, hybrid, and explainable models. These methods are increasingly applied to complex project datasets that include project size, contract type, labour productivity, material quantities, BIM outputs, site records, and planning information. Across the literature, artificial intelligence demonstrated strong potential for improving both cost estimation and schedule prediction, particularly in situations where traditional approaches are constrained by incomplete data, static assumptions, and subjective judgment. However, the review also found that model performance varies considerably depending on data quality, feature selection, validation strategy, and the extent to which training data reflect real project conditions. The evidence base remains concentrated in digitally mature settings, while Nigerian studies are comparatively limited and often focus more on readiness, barriers, and adoption conditions than on direct predictive modelling. In the Nigerian context, key constraints include weak data structures, limited technical capacity, poor digital integration, inadequate infrastructure, and resistance to organisational change. At the same time, important enablers were identified, including leadership commitment, training, policy support, and gradual integration of artificial intelligence into existing project workflows. Overall, the review concludes that artificial intelligence offers a credible route for improving construction cost and schedule prediction, but its usefulness in Nigeria will depend on stronger data systems, local validation, explainable modelling, and phased implementation. Future research should prioritize Nigeria specific datasets, real project pilots, and BIM-integrated forecasting systems that support reliable and context sensitive decision making.