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Precise tracking and forecasting of dissolved oxygen (DO) concentrations in aquaculture environments are essential for sustaining ideal water quality and safeguarding fish well-being. This research introduces an improved Long Short-Term Memory (LSTM)-based framework for DO forecasting that leverages historical records of DO concentrations, water temperature, and other environmental variables. In contrast to conventional techniques that rely on static assumptions, the proposed framework dynamically adapts to changing environmental conditions, delivering immediate, high-accuracy predictions. Experimental results show that the improved LSTM framework achieves a forecasting precision of 92.28%, surpassing current models, including IFP (81.97%), DOE (63.81%), and DOP (86.79%). The framework's exceptional precision and flexibility make it a reliable tool for aquaculture oversight, helping optimize DO levels and reduce the risk of fish deaths. By incorporating artificial intelligence into aquaculture surveillance, this method enhances system performance and long-term viability.

Food supply chains face growing pressure to reduce waste, protect product quality, and improve sustainability performance. Yet many supply chain decisions remain reactive, even though food products are perishable, demand is volatile, and quality deterioration often begins before problems become visible. This article proposes that food systems require a more integrated predictive orientation rather than isolated technological improvements. The central problem is that demand forecasting, waste analytics, cold-chain monitoring, and sustainability assessment are often developed as separate capabilities. When these tools remain disconnected, prediction does not necessarily translate into coordinated action or measurable environmental improvement. The absence of an integrated model limits the ability of food supply chains to anticipate waste, preserve quality, and learn from sustainability outcomes. The objective of this article is to propose a conceptual model of Predictive Food Systems. The model connects demand forecasting, waste reduction and inventory intelligence, cold-chain intelligence, and sustainability performance within a closed-loop architecture. It argues that predictive value emerges when data from each component continuously inform decisions across the whole system. The article is based on a conceptual synthesis of 26 peer-reviewed journal articles published between 2017 and 2025. These studies cover food logistics, artificial intelligence, demand forecasting, dynamic inventory management, cold-chain monitoring, food waste accounting, and sustainability assessment. The article does not report new empirical data but uses existing evidence to construct an original systems-oriented framework. The proposed model specifies how demand prediction can reduce overproduction, how inventory intelligence can redirect or discount perishable stock, how cold-chain intelligence can preserve shelf-life, and how sustainability metrics can feed back into predictive systems. Three tables organise the model’s forecasting approaches, cold-chain technologies, and integrated components. The article concludes that predictive food systems can become a practical pathway for reducing waste, improving quality, and advancing sustainability performance.