Artificial Intelligence and Machine Learning in Utility Infrastructure Financial Forecasting

The growing complexity and instability of utility infrastructure systems have undermined the efficiency of traditional econometric techniques of financial forecasting. This review is a literature survey of artificial intelligence (AI) and machine learning (ML) that is reinventing financial forecasting in the energy, water, and smart urban infrastructure industries. It bases itself on peer-reviewed literature written since 2015 and 2025 to synthesize theoretical background, methodological advances, and research applications to assess AI-based models integration in utility finance. The most recent developments in deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), or reinforcement learning models make it feasible to model nonlinear, spatio-temporal, and adaptive dynamics in infrastructure systems. It has been empirically proven that AI-based and hybrid forecasting models are more accurate, robust, and capable of predicting over multi-horizons than more traditional statistical methods. In addition, AI-driven forecasting is a paradigm shift of the unchanging econometric prediction to dynamic financial intelligence to aid real-time decision-making, risk reduction, and capital optimization. However, there are still serious issues related to data interoperability, algorithmic transparency, and governance alignment in the context of the public utility.