Understanding how conflict events spread over time and space is crucial for predicting and mitigating future violence. However, progress in this area has been limited by the lack of methods capable of capturing the intricate, dynamic patterns of conflict diffusion. The complex nature of those trends needs flexibility in the models to untangle them. This study addresses this gap by analyzing spatio-temporal conflict fatality data using an innovative approach that transforms the data into three-dimensional patterns at the PRIO-grid level. First, our research investigates pattern redundancy and identifies potentially dangerous patterns preceding conflict outbreaks. By applying the Earth Mover’s Distance (EMD) algorithm, we detect and classify these patterns, allowing us to compare and match patterns with high adaptive capacity in all dimensions. Unlike traditional methods that rely on individual data points, our approach examines patterns across time and space, enabling a deeper understanding of conflict dynamics. Using historical similar patterns, we generate predictions of conflict fatalities and compare these with forecasts from the VIEWS ensemble model, a leading benchmark. Our findings demonstrate that recognizing and analyzing conflict diffusion patterns significantly improves predictive accuracy, outperforming the benchmark model. This research contributes to the study of conflict dynamics by introducing a novel pattern recognition framework that enhances the analysis of spatio-temporal data and offers practical applications for early warning systems.