Artificial intelligence (AI) is reshaping maintenance for conventional, electric (EV), and autonomous vehicles (AV). By leveraging on-board sensors and the Internet of Vehicles (IoV), AI enables a shift from reactive repairs to predictive strategies. Machine learning (ML) and deep learning (DL) models now support continuous health monitoring. These models utilize data from diverse sources, including Digital Twins (DTs) and low-latency V2X networks, to detect faults early. Recent studies (Hossain et al. in Measurement 253, 2025) report up to 95% fault-detection accuracy for conventional vehicle subsystems under controlled benchmarks. Similarly, diagnostics for AV perception show high reliability in specific test scenarios. In the EV domain, simulation-based evaluations (Hu et al. in Energy Convers Manag 300, 2024; Kermansaravi et al. in Energy Rep 13:5535–5550, 2025) indicate potential energy efficiency improvements of 15–20% and battery lifespan extensions of 10–15% compared to baseline methods. Additionally, AI-enabled control strategies in heavy-duty fleets may reduce operating costs by 31.4–40.5%, as observed in optimized microgrid case studies (Hu et al. in Ad Hoc Net 146, 2023; Kumar and Prabhansu in Future Batter 7:100087, 2025). Unlike broad technological overviews, this chapter presents a novel Task–Data–AI–KPI mapping. This framework connects maintenance objectives to specific data requirements and algorithmic choices. It situates these strategies within a layered infrastructure architecture—spanning edge sensors to cloud-based digital twins—to demonstrate their impact on key performance indicators (KPIs) such as availability and lifecycle costs.