Off-highway vehicles include a wide variety of machines used in major industries such as agriculture, construction, and forestry. Uptime of these vehicles is vital, as they generate revenue only when operational in the field. Knowing in advance when a component will fail prevents downtime costs, maintenance expenses, and sometimes serious human injury. Poor maintenance strategies can reduce productivity by up to 20%. OEMs invest significant funds in generating data (temperature, pressure, vibration, load, speed, etc.) from almost all off-highway vehicles before and after production. This data can be used to develop advanced predictive maintenance (PdM) methods for these vehicles. By utilizing data and advanced AI/ML-based analytics, PdM can predict failures well in advance, helping to avoid catastrophic component breakdowns and maximize vehicle uptime in the field. The main aim of PdM is to optimize RAMS (Reliability, Availability, Maintainability, and Safety) for off-highway vehicles by leveraging sensor technologies. In this paper, we discuss existing PdM approaches, avenues for improvement, and implementation strategies for off-highway vehicles, with a particular focus on tractors. The paper provides an overview of how effective AI/ML-based PdM for off-highway vehicles (tractors) can reduce maintenance costs by up to 25%, eliminate breakdowns by up to 70%, reduce downtime by up to 50%, and improve repair scheduling by up to 12%.