Abstract: Live fish transportation is a critical link between aquaculture production and consumer markets, directly determining fish survival rate, product quality, commercial value, and the overall operational efficiency and long-term sustainability of modern aquatic supply chains. Transport environments are highly unstable and susceptible to sudden changes, where fluctuations in dissolved oxygen, pH, temperature, total ammonia nitrogen, and carbon dioxide, together with unsuitable stocking density and persistent in-transit mechanical disturbances, easily induce severe physiological stress and abnormal behavioral responses in fish. Traditional monitoring methods rely heavily on intermittent manual detection and empirical on-site observation, which cannot achieve real-time and accurate perception of latent fish stress, resulting in delayed intervention before fish health and product quality gradually degrade. Furthermore, simple static environmental threshold standards set by existing industry standards fail to fully reflect fish physiological status, as stress tolerance varies significantly with fish species, body size, and complex transport conditions such as low-temperature waterless delivery. This review establishes a three-level framework covering sensing perception, algorithmic cognition, and system integration to systematically summarize current progress and practical limitations of intelligent monitoring technologies for live fish transportation. Advanced multi-source sensing technologies realize comprehensive status monitoring through the integration of water physicochemical parameter measurement, individual physiological detection, and group behavioral analysis. Water quality sensors continuously track key environmental indicators, though vulnerable to fouling and baseline drift under humid, bumpy vehicle conditions; inertial sensors and computer vision quantitatively characterize fish swimming behaviors, escape responses, respiratory features, and spatial distribution. Complementary physiological monitoring captures core biological indicators including heart rate, respiratory frequency, skin impedance, and mucus biomarkers alongside blood glucose levels. In particular, non-invasive flexible sensors exhibit unique advantages of high conformability, low biological interference, and sustainable continuous monitoring, enabling effective capture of transient in-transit stress responses. Machine learning algorithms support multimodal extraction and fusion of environmental, behavioral, and physiological data, converting fragmented sensing information into systematic fish health evaluation. This approach achieves early latent stress identification and accurate survival risk prediction, providing reliable data support for refined transport management. Current practical obstacles include species-specific differences, varied fish body sizes, dynamically changing stocking loads, complex sensing scenarios, and the insufficient interpretability, robustness, and cross-scenario generalization of existing lightweight models deployed on vehicle terminals. System-level technologies further empower intelligent transportation monitoring: the Internet of Things constructs a unified information hub for data collection, analysis, decision-making, and environmental regulation; edge computing ensures real-time local anomaly identification and early warning without heavy cloud reliance; blockchain enables tamper-proof data storage, secure information sharing, and full-process traceability across the whole aquatic supply chain. The integrated application of these technologies builds a complete closed-loop management system, transforming traditional passive post-event monitoring into proactive stress intervention and precise environmental control. Despite existing technological progress, large-scale industrial promotion still faces prominent technical bottlenecks such as high hardware maintenance cost and unstable signal connection on long-distance cross-regional routes. Future research should focus on the deep integration of multi-source sensing, artificial intelligence, and digital communication technologies, while optimizing sensor self-cleaning performance, biocompatibility, long-term operational stability, and model environmental adaptability. Breaking through these limitations will facilitate the development of adaptive intelligent transport systems with real-time stress assessment and precise regulatory capabilities, promoting the intelligent upgrading of live fish transportation and effectively improving fish welfare, transport efficiency, product quality, and the sustainable development of aquatic product supply chains.