<p>Effective nitrogen removal is a primary objective for municipal wastewater treatment plants (WWTPs). Although intelligent models offer promising solutions to challenges such as fluctuating effluent total nitrogen (TN) levels and high operational costs in conventional methods, interpreting feature interactions within these “black-box” models and formulating efficient TN control strategies remain major challenges. We applied interpretable approaches, SHapley Additive exPlanations (SHAP) and structural equation modeling (SEM), to assess feature importance and interaction mechanisms. Feature fusion and dimensionality reduction yielded two-dimensional predictors, which were incorporated into a reinforcement learning model to optimize effluent TN in municipal wastewater treatment plants. Results showed significant seasonal differences in parameter distributions (<i>p</i> &lt; 0.01), with both the magnitude and pathways of their influence on effluent TN varying across seasons (SHAP values 0–3). Linear and nonlinear interactions produced 77 candidate features, from which pH, aeration, and DO_3 were retained for model integration. A genetic algorithm was then used to optimize biochemical base models, resulting in a GA_Stacking prediction model (<i>R</i>² &gt; 0.77). To enable adaptive TN control, we further developed a Stacking-based Genetic Algorithm for Q-learning (SGAQ) framework: treating the GA_Stacking model as the “environment” and using sludge return flow and aeration rate as control “actions.” After optimization, effluent TN consistently met discharge standards, with an average reduction of 7.95%. This study presents a robust predictive model and a seasonally adaptive optimization strategy, offering a novel approach to effluent TN management in WWTPs.</p>