A quaculture is facing increasing pressure from environmental challenges, diseases, and the demand for higher production. In the face of these growing challenges, effective biosecurity management plays essential role as intensive production practices causes increased biosecurity risks. A quaculture biosecurity management is a key concept to prevent and control diseases and to maintain a good environment in aquaculture production systems. It consists of practices that minimize the risk of introduction and spread of an infectious disease . A robust biosecurity system helps in maintaining the health of aquaculture stocks, preventing the spread of diseases, and ensuring the overall quality of the output. In the era of Big Data and AI, various digital technologies have emerged to offer potentials of smart biosecurity management based on better control of water quality, feeding and disease . A main challenge for the successful and wide adoption of effective smart aquaculture technologies is the lack of an integrated approach for effective biosecurity management decision support systems to assist farmers . For example, t here is a lack of capability to accurately predict how water condition is affected by the environment condition, management practice and the growth of cultured species i n time for precision c ontrol of production condition and disease prevention . This research proposes a framework (see below) for the development and integration of biosecurity intelligent decision support toolkits, IoT sensors, AI-driven biosecurity system modelling, and cloud-based data analytics to optimize aquaculture biosecurity measures and decision support . The proposed system also involves using novel physiologically-based modelling techniques.