<p>Mycobacteriosis is a chronic fish disease that poses significant challenges to aquaculture sustainability and marine biodiversity. Existing prediction methods that employ machine and deep learning often lack accuracy, interpretability, and scalability, while overlooking temporal dependencies in water-quality parameters. These shortcomings lead to delays and inefficiencies in responding to disease outbreaks. To address these issues, this study proposes a deep-reinforcement-learning model that combines Bidirectional Gated Recurrent Units (BIGRU) with the Asynchronous Advantage Actor–Critic (A3C) algorithm. Using the Mycobacteriosis Disease Water Quality Index (MWQI), the model classifies water-quality conditions and identifies disease outbreaks by analyzing key indicators such as DO, pH, temperature, and ammonia. The model operates in two stages: the BIGRU module captures short- and long-term dependencies in sequential water-quality data through bidirectional processing, ensuring that both past and future contexts are considered. The A3C algorithm, with its asynchronous learning mechanism, provides robust real-time decision-making by optimizing cumulative rewards to classify conditions as “disease” or “non-disease,” thereby addressing the dynamics of aquaculture environments. An explainability module based on SHAP is integrated to quantify the contribution of each water-quality parameter to the predictions, thereby enhancing transparency. The proposed BIGRU–A3C model achieves accuracies of 99.93% and 98.69% on the real-time and repository-based datasets, respectively. Additional evaluation metrics affirm its robustness, with average rewards (0.993 and 0.9932), actor–critic losses (0.001 and 0.005), and advantage-utilization values (0.95 and 0.94) on the respective datasets. These results underscore the model’s reliability in managing complex aquaculture environments while ensuring interpretability and trust for industry stakeholders.</p>