The increasing demand for reliability, safety, and efficiency across complex engineering systems has placed Prognostics and Health Management (PHM) at the centre of industrial innovation. In particular, accurately estimating Remaining Useful Life (RUL) is crucial for predicting failures and extending system lifecycles. The progressive integration of Artificial Intelligence (AI) into PHM has reshaped the way reliability and maintenance are addressed in engineering systems emerging as a key enabler of advanced prognostic methods and complementing or surpassing traditional physics-based models. This thesis presents a systematic literature review (SLR) on emerging approaches for AI-based prognostics, analysing fifty-four peer-reviewed studies across multiple engineering domains including aerospace, mechanical equipment, manufacturing, wind energy, and automotive.