Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in
wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting
their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents
complex challenges that require specific models, extensive training data, and addressing issues of
generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep
Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA
systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This
study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement
Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation,
user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning-based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI
and DL show promise in improving user performance and promoting fair power distribution in
NOMA systems. This study emphasizes the significance of continuous research efforts to overcome
current obstacles, enhance efficiency, and strengthen the dependability of wireless communication
systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning
and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving
fair power distribution in NOMA systems. However, further investigation is crucial to addressing
obstacles and fully exploring the capabilities of NOMA technology
