In the context of 6G, the integration of Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) is crucial for achieving worldwide, uninterrupted access. The convergence of both is particularly problematic for the following reasons: architectural diversity, signal interference, differences in latency, and complex processing of signals. The complexity resulting from these issues can be addressed through intelligent automation, adaptive resource allocation, and predictive analytics, for which Artificial Intelligence (AI) has come to provide solutions. This chapter aims to show how various AI techniques, including but not limited to Machine Learning (ML), Deep Learning (DL), Federated Learning (FL) and Reinforcement Learning (RL), are used to improve the functionality and dependability of integrated TN and NTN systems. It analyzes AI-driven techniques that aim to optimize coverage extension and energy consumption. Analysis of real-world case studies from disaster response, maritime logistics, smart farming, and national security illustrates the impact of these technologies. The chapter wraps up with a conversation about persistent issues and prospective research scopes such as explainability, governance, andalignment with regulations. In general, the integration of AI into TN and NTN systems presents an optimal transforming solution towards building a resilient, intelligent, and inclusive global communication infrastructure.