<p>Security, healthcare, and human–computer interaction applications require accurate gait identification under complex environmental conditions such as varying lighting and background noise. Current approaches are usually unable to adapt to dynamic, high-dimensional environments, with reduced feature extraction and classification accuracy. This paper bridges the gap by offering an overview of a multi-stage framework that merges the advanced techniques of machine learning with those of reinforcement learning for preemptive optimization. It begins by using a Deep Deterministic Policy Gradient (DDPG) for a preprocessing module: environmental parameters are dynamically adjusted to optimize their real-time data quality. The module is then followed by a phase in multi-domain feature extraction using Sparse Group Lasso along with KMeans clustering, thereby improving representativeness while reducing dimensionality by 50–60%. We have used a hybrid of stacked generalization, in this case of XGBoost and LightGBM, because this provides better classification accuracy. Refined temporal post-processing at the hidden Markov model and Auto-Regressive Integrated Moving Average (ARIMA) results in enhanced phase transitions that may be gait-based, thus improving the identification accuracy. As the final step, we use Proximal Policy Optimization (PPO) to implement feedback-driven reinforcement learning, where improvements are incrementally made by updating the model with iterative feedback. This new method enhances the correctness of feature extraction by 12% in complex environments. Overall classification accuracy increases by 5–6% and reaches 95%. False positives in gait phase transitions also decrease, increasing system robustness and reliability in real-world applications.</p>