Amyotrophic Lateral Sclerosis is a progressive neurodegenerative disease with high genetic heterogeneity and limited interpretable biomarkers for clinical use. While machine learning models trained on transcriptomic data have shown promising classification performance, their black-box nature limits biological insight and clinical translation. In this study, we present a focused interpretability analysis using Shapley Additive Explanations applied to a tree-based ALS classifier. Our objective is to investigate SHAP’s utility in identifying gene-level contributors to ALS diagnosis, with a particular emphasis on the underexplored genes, such as BIN2. Through SHAP value analysis on transcriptomic features, BIN2 emerged as a consistently high-ranking gene alongside known ALS markers such as CYTIP and SASH3. Literature review suggests BIN2’s association with neuroimmune and mitochondrial pathways implicated in neurodegenerative processes. These findings highlight the power of SHAP to extract biologically meaningful signals from complex models and suggest that BIN2 may serve as a candidate biomarker for ALS. Unlike model-centric studies, this work isolates the interpretability layer to demonstrate how post hoc analysis can independently drive hypothesis generation and gene prioritization in precision medicine contexts.