This conclusion highlights key insights from the study presented, emphasizing the evolving landscape of gait recognition from traditional machine learning to deep learning-based methods. While identifying ongoing challenges such as data quality, ethical considerations, and architectural limitations, this chapter points towards the promising convergence of model-free and model-based approaches. This amalgamation is poised to drive future innovations, enhancing the accuracy and robustness of gait recognition systems for diverse real-world applications. It concludes by outlining potential avenues for future research and development in the field.