The estimation of fish ages and growth through otoliths is crucial to ensuring a sustainable management and conservation of exploited marine fish because it informs fisheries assessment models and management strategies. A traditional method of analyzing otolith images is labor-intensive, requires special skills, and can incur substantial costs. Advances in artificial intelligence (AI) have led to deep learning models that automate the age estimation process from otolith images, increasing accuracy, reducing costs, and increasing throughput. This systematic review overviews the effectiveness, performance, and limitations of various machine learning techniques applied to automated fish age extraction from otolith images, identifying emerging themes for future research aimed at improving accuracy in age estimation across diverse fish species. The findings indicate a significant focus in the literature on leveraging AI to revolutionize the counting of growth rings on otoliths, thereby mitigating human error and enhancing the monitoring and management of fishery resources through improved species detection, identification, and classification.