This paper explores the integration of the belief function theory within the domain of Prognostics and Health Management (PHM), offering a novel approach to decision-making under conditions of uncertainty and incomplete information. Central to our methodology is the modeling of beliefs and uncertainties through belief mass functions, enabling the representation and aggregation of diverse information source. This approach is particularly advantageous in dynamic and complex environments characteristic of PHM, where data may be partial or conflicting. By adjusting the weight of information based on source reliability, our framework supports nuanced and adaptive decision-making. Furthermore, the proposed model facilitates collaborative decision-making in distributed systems by effectively managing information diversity and resolving conflicts. While focused on PHM, the versatility of our approach, experimented through numerical examples, allows for potential applications across various fields requiring robust and adaptive decision-making strategies in the face of uncertainty.