<p>This study examines tree-based and parametric machine learning models to simulate and predict the dynamics of sensitive and resistant bacterial populations in response to antibiotic and predation-based interventions. Synthetic datasets, created from discretized ordinary differential equations, capture nonlinear population shifts and are used to train the models. Model performance is compared based on how accurately key ecological behaviors are reproduced. These behaviors include resistance emerging under sustained antibiotic pressure and its suppression with combination interventions. The study introduces a hybrid approach that merges mechanistic modeling with machine learning models. This enables ecologically grounded predictions and allows efficient exploration of adaptive treatment strategies. An adaptive threshold control mechanism, inspired by feedback control theory, is embedded in the ML models to guide intervention decisions dynamically. This approach accurately forecasts bacterial resistance, enabling the design and evaluation of responsive treatment strategies. Overall, the study highlights the promise of ML-informed hybrid models for adaptive antimicrobial stewardship. These models offer scalable and interpretable tools to guide future therapies against antimicrobial resistance.</p>