Predicting the spread of processes across complex multi-layered networks has long challenged researchers due to the intricate interplay between network structure and propagation dynamics. Each layer of these networks possesses unique characteristics, further complicating analysis. To authors' knowledge, a comprehensive framework capable of simulating various spreading processes across different layers, particularly in networks with millions of nodes and connections, has been notably absent. This study introduces a novel framework that efficiently predicts Markov Chain processes over large-scale networks, while significantly reducing time and space complexity. This approach enables exact simulation of spreading processes across extensive real-world multi-layer networks, accounting for diverse influencers on each layer. FastGEMF provides a baseline framework for exact simulating stochastic spread processes, facilitating comparative analysis of models across diverse domains, from epidemiology to social media dynamics.