The rapid evolution of mobile communication systems has brought the research community to the frontier of the sixth-generation wireless networks. The main aim is to bring a network with high data rates, ultra-low latency, massive device connectivity, and high energy efficiency to the real world. 6G represents a technological shift with significant implications for network design and performance optimization. However, as this network operates at extremely high frequencies, this makes the researchers face a lot of challenges, such as modeling, simulation, and analysis, primarily due to the scarcity of real-world deployment and empirical data. This dissertation studies the 6G network to address this gap, as it proposes a comprehensive framework that applies advanced data processing techniques and artificial intelligence integration strategies to better understand and model 6G communication network behaviors. This work begins by simulating a key MAC layer mechanism known as Adaptive Directional Antenna Protocol for Terahertz, which is specifically tailored for the 6G context. Using the ns-3 simulator integrated with the TeraSim module, we reproduce directional communication scenarios within THz bands. The simulation captures a range of critical Key Performance Indicators such as throughput, delay, packet collision rate, and received power. These KPIs are not only vital for characterizing network performance but also serve as the fundamental input for downstream processing and learning mechanisms. Following data extraction, a comprehensive preprocessing stage is conducted to extract the hidden informative features and evaluate the complexity of the network behavior. This step includes both classical signal processing and advanced techniques such as Shannon entropy for quantifying uncertainty, empirical mode decomposition and ensemble variant empirical mode decomposition for adaptive multi-scale decomposition, and the marginal Hilbert spectrum for energy distribution in the time-frequency domain. By doing so, a nuanced behavioral dynamic is revealed that is otherwise hidden in raw performance logs. The third layer of the methodology involves the application of five AI strategies to leverage the processed data, including unsupervised machine learning methods, supervised learning models, multi-layer transfer learning, generative AI techniques, and reinforcement learning, particularly deep Q-networks. Importantly, given the absence of real-world 6G testbeds and empirical data, the dissertation emphasizes theoretical validation. Comparative metrics, entropy-based measurements, and behavioral symmetry analyses are used to assess the coherence and plausibility of the simulated and AI-generated results. The proposed pipeline thus ensures that each stage from simulation to learning is grounded in logical consistency and system behavior modeling principles. This dissertation offers a multi-layered framework that advances the current state of research in 6G network modeling by bridging simulation, complexity analysis, and artificial intelligence. The methodology developed herein is not only applicable to 6G research but is also generalized to other high-frequency, high-complexity wireless systems. It lays a robust foundation for future work that will inevitably benefit from the availability of real-world 6G deployment data.