Prognostic methods broadly fall into two categories—model-based and data-driven—both of which have shown effectiveness across a range of engineering applications. Model-based approaches require an explicit representation of the degradation process, defining failure as the point when the physical damage state exceeds a predetermined threshold. Data-driven methods, on the other hand, leverage sensor data to directly predict end-of-life (EOL) or related prognostic information. Although both approaches offer insights that could be complementary and potentially fused, most existing fusion methods either combine the outputs from multiple methods or adopt a data-driven method to assist the model-based method. To further enhance the prognostic performance, this study proposes a fusion-based prognostic approach in which the output of one method is actively used to update the model of the other through either the crossover operator or the likelihood function. The proposed approach is validated using both an aluminum fatigue dataset and the Prognostics and Health Management (PHM) 2010 cutter wear dataset, demonstrating improved prognostic accuracy compared to either method used independently.