This review article explores the advancements in artificial intelligence (AI) techniques for cochlear implants (CIs), focusing on automatic speech recognition (ASR) and speech enhancement. The authors highlight the challenges in traditional signal processing methods, particularly in noisy environments and multiple speech sources, and discuss how AI methods are addressing these issues. The review covers the latest developments in deep learning (DL) algorithms, such as transformers and reinforcement learning (RL), and their applications in CI-based ASR. It also provides a comprehensive taxonomy of AI techniques used in CI, including machine learning (ML) and DL methodologies, and discusses the available datasets and key metrics for evaluation. The article further delves into the medical applications of AI in CI, such as denoising, speech enhancement, segmentation, and imaging, and addresses existing research gaps and future directions. The motivation behind the review is to provide a detailed analysis of recent AI-based CI frameworks, offering valuable insights for researchers, clinicians, and technologists involved in CI development and improvement. The review concludes with implications and future research directions, emphasizing the potential for transformative breakthroughs in CI technologies.This review article explores the advancements in artificial intelligence (AI) techniques for cochlear implants (CIs), focusing on automatic speech recognition (ASR) and speech enhancement. The authors highlight the challenges in traditional signal processing methods, particularly in noisy environments and multiple speech sources, and discuss how AI methods are addressing these issues. The review covers the latest developments in deep learning (DL) algorithms, such as transformers and reinforcement learning (RL), and their applications in CI-based ASR. It also provides a comprehensive taxonomy of AI techniques used in CI, including machine learning (ML) and DL methodologies, and discusses the available datasets and key metrics for evaluation. The article further delves into the medical applications of AI in CI, such as denoising, speech enhancement, segmentation, and imaging, and addresses existing research gaps and future directions. The motivation behind the review is to provide a detailed analysis of recent AI-based CI frameworks, offering valuable insights for researchers, clinicians, and technologists involved in CI development and improvement. The review concludes with implications and future research directions, emphasizing the potential for transformative breakthroughs in CI technologies.