This review provides a comprehensive overview of artificial intelligence (AI) strategies, challenges, and future directions in cochlear implant (CI) research. Automatic speech recognition (ASR) plays a crucial role in CI technology, enabling speech perception and communication for individuals with hearing impairments. Despite advancements in signal processing techniques, challenges persist, particularly in noisy environments and with multiple speech sources. AI methods have emerged as promising solutions to address these limitations, offering enhanced speech enhancement and processing capabilities. The review discusses various AI techniques, including machine learning (ML), deep learning (DL), and neural networks, and their applications in CI-based ASR and speech enhancement. It also explores the use of AI in CI programming, electrode placement, and noise reduction. The review highlights the importance of high-quality datasets, algorithm transparency, and the need for further research to improve CI performance. Additionally, it addresses the potential of AI in biomedical applications, such as speech recognition, event detection, and source separation. The review also discusses the use of AI in predicting CI outcomes, optimizing electrode placement, and improving speech intelligibility. The study emphasizes the importance of interdisciplinary approaches, combining neurobiology, signal processing, and medical technology, to advance CI technology. The review concludes with a discussion on future research directions, including the development of more efficient AI algorithms, the integration of AI with other technologies, and the need for standardized evaluation metrics. Overall, the review aims to provide a thorough understanding of AI applications in CI research and to guide future research and development in this field.This review provides a comprehensive overview of artificial intelligence (AI) strategies, challenges, and future directions in cochlear implant (CI) research. Automatic speech recognition (ASR) plays a crucial role in CI technology, enabling speech perception and communication for individuals with hearing impairments. Despite advancements in signal processing techniques, challenges persist, particularly in noisy environments and with multiple speech sources. AI methods have emerged as promising solutions to address these limitations, offering enhanced speech enhancement and processing capabilities. The review discusses various AI techniques, including machine learning (ML), deep learning (DL), and neural networks, and their applications in CI-based ASR and speech enhancement. It also explores the use of AI in CI programming, electrode placement, and noise reduction. The review highlights the importance of high-quality datasets, algorithm transparency, and the need for further research to improve CI performance. Additionally, it addresses the potential of AI in biomedical applications, such as speech recognition, event detection, and source separation. The review also discusses the use of AI in predicting CI outcomes, optimizing electrode placement, and improving speech intelligibility. The study emphasizes the importance of interdisciplinary approaches, combining neurobiology, signal processing, and medical technology, to advance CI technology. The review concludes with a discussion on future research directions, including the development of more efficient AI algorithms, the integration of AI with other technologies, and the need for standardized evaluation metrics. Overall, the review aims to provide a thorough understanding of AI applications in CI research and to guide future research and development in this field.