<p>The precise forecasting of the Remaining Useful Life (RUL, expressed in hours) of aircraft engine-like systems using a synthetic dataset designed to mimic real engine degradation patterns is a key factor in setting up maintenance schedules that are cost-efficient and safety-enhancing. Traditional fixed-interval maintenance often results in inefficient resource allocation and increased risk of unscheduled maintenance events due to the complex, nonlinear nature of engine deterioration. Consequently, a significant portion of maintenance resources is used sub-optimally. Maintenance scheduled at fixed intervals is often inefficient in resource allocation and, at the same time, a source of unplanned failures due to the complex, nonlinear nature of the engine deterioration process. Addressing such issues requires highly powerful prediction models capable of tracking the complex patterns of engine deterioration through multiple sensor readings. This publication describes a hybrid strategy that combines Light Gradient Boosting Regression (LGBR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Besides that, Recursive Feature Elimination (RFE) was used to identify features that would contribute most to the model's structure, thereby indirectly facilitating its interpretability of impact. The data used in the experiment show that Usage_Time_Hours (total accumulated operating hours of the engine) and Vibration_mm_s (measuring the vibration level within the engine system) should be the most predictive features of RUL. Among all the models, the Light Gradient Boosting Regression optimized with Genetic Algorithm (LGGA) achieved the best performance, with an R2 of 0.983 and an RMSE of 432.77. Furthermore, the simulation of real-time engine health monitoring and predictive maintenance has verified the LGGA model's ability to quickly reach a conclusion, exhibit minimal residual bias, and achieve high predictive accuracy.</p>