In the final week, the machine learning pipeline was fully integrated into the system architecture, enabling end-to-end data processing and prediction. On top of this foundation, a genetic algorithm was implemented as the core planning module, encoding decisions as chromosomes optimized through a fitness-driven evolutionary process. The tight coupling between the GA and the ML models allowed learned probabilities to directly guide planning decisions. After validating the integrated system, the final results were consolidated and the project was submitted.