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Predictive maintenance plays a crucial role in the context of Industry 4.0, and the adoption of Digital Twin methodologies has emerged as a promising approach for predicting the remaining useful lifetime of assets, particularly after a fault is identified. However, there is a lack of understanding regarding how to effectively apply digital twins for prognosis purposes, including estimating confidence intervals and identifying root causes of faults. To address this gap, this paper presents a methodology based on a comprehensive literature review, aiming to provide a systematic approach for predicting the remaining useful lifetime of assets. The proposed methodology encompasses several steps. It starts with data collection from physical assets or relevant databases, followed by modeling the asset’s behavior using dynamic equations. Machine learning algorithms are then applied to predict the asset’s final state in response to corrective actions.