Reliability and predictive maintenance play a crucial role in the aerospace sector, where safety and operational efficiency depend on the ability to predict and mitigate the degradation of critical components. In this context, prognostics emerges as an innovative approach for estimating the Remaining Useful Life (RUL) of a component and for planning targeted maintenance interventions, reducing operational costs and improving system availability. This thesis first introduces the concept of prognostics, analyzing its main methodologies and potential future applications in the field. The integration of prognostic algorithms, employing artificial intelligence, into modern aircraft represents a crucial step toward the development of more efficient maintenance strategies capable of extending component useful life without compromising safety.