<p>The health and welfare of cultured fish are critical determinants of aquaculture productivity and sustainability. Emerging applications of artificial intelligence (AI) are transforming aquatic animal health management by enabling early disease detection, predictive diagnostics, precision feeding, and real-time water quality monitoring. This narrative review synthesizes current advancements in AI-driven approaches that support fish health improvement, including computer vision-based disease recognition, machine learning algorithms for biomarker analysis, and integrated AI–IoT systems for continuous health surveillance. Our synthesis indicates that AI technologies offer substantial potential to reduce mortality, enhance biosecurity, and support welfare-centered management practices. However, barriers such as fragmented datasets, limited interpretability of models, and challenges in practical adoption still constrain large-scale application. To fully realize the benefits for fish health, future research must prioritize the development of standardized diagnostic datasets, lightweight and farmer-accessible tools, and integrative frameworks that link AI with veterinary expertise and sustainable farming practices. This review underscores that AI is not merely an auxiliary tool but a pivotal innovation for advancing fish health and welfare in modern aquaculture. It provides a veterinary-oriented perspective, aiming to guide researchers, practitioners, and policymakers towards the responsible and effective integration of AI in aquatic animal health management. This article adopts a narrative review approach to synthesize current advances and future directions in AI-based fish disease diagnosis and management.</p>