This project focuses on simulating the organic growth of criminal communication networks by leveraging techniques such as Reinforcement Learning and Graph Theory. The goal is to curate a synthetic dataset that models the evolving structure and dynamics of illegal networks, taking into account factors like social connections, communication patterns, and resource allocation. By using graph-based models, the project aims to create realistic representations of how criminal groups form, expand, and operate under various conditions. Machine learning, specifically Reinforcement Learning, will help simulate decision-making processes within these networks, enabling them to adapt and evolve over time. This tool will provide valuable insights for law enforcement, cybersecurity teams, and policy makers by offering a deeper understanding of criminal networks and their behavior. By synthetically modeling these environments, it will assist in developing more effective strategies for prevention, detection, and dismantling of criminal organizations.