The Nigerian construction and energy sectors face persistent challenges related to cost overruns, schedule delays, inefficient resource utilisation, and rising carbon emissions. These challenges are exacerbated by fragmented data systems, manual planning practices, and limited adoption of advanced digital tools. This paper explores the application of AI-driven predictive analytics as a strategic solution for optimising resource allocation, project scheduling, and carbon performance in Nigerian construction and energy projects. By leveraging machine learning algorithms, historical project data, real-time sensor inputs, and geospatial information, predictive analytics can forecast material demand, labour productivity, equipment usage, schedule risks, and emissions trajectories with greater accuracy. The study examines how AI-enabled models support proactive decision-making, reduce waste, enhance time efficiency, and align projects with national sustainability and energy transition goals. Particular attention is given to the Nigerian context, including data availability constraints, infrastructural limitations, regulatory considerations, and capacity gaps. The paper argues that integrating predictive analytics into project management and energy planning frameworks can significantly improve project delivery outcomes while supporting low-carbon development pathways. It concludes by highlighting implementation pathways, governance requirements, and policy implications necessary to scale AI-driven optimisation across Nigeria’s construction and energy value chains.