I. Introduction  
A. Contribution  
B. Paper Organization  

II. Background on NTN  
B. Role of NTN in 6G  
C. General Architecture  
D. Fundamental Characteristics of NTN  
E. Challenges Associated with NTN  

III. AI and Its Relevance to NTN Challenges  
A. Machine Learning (ML)  
B. Offline vs Online Learning  
C. Deep Learning (DL)  
D. Major Learning Paradigms  
E. Distributed Learning Paradigms  

IV. Related Works  

V. AI-NTN: Current Research Thrusts  
A. Taxonomy of Research Thrusts  
B. Physical Layer Aspects  
C. Data Link Layer Aspects  

VI. AI-NTN Integration: Current Status  
A. ML Testbeds for Satellite Networks  
B. AI-NTN Integration through O-RAN-based RIC  
C. Current Research Efforts for AI-NTN Integration through O-RAN-based RIC  

VII. AI-NTN Integration: Challenges  
A. Limited Onboard Capability  
B. Aging of Information  
C. Additional Communication Overheads  
D. Security Aspects  
E. Environmental Conditions  
F. Scalability Issue  
G. Lack of Convergence  
H. Scarcity of Quality Data  
I. Complicated Hyperparameter Settings  
J. Lack of Generalization  

VIII. Insights and Potential Future Studies  
A. Insights  
B. Potential Future Studies  

IX. Conclusion