## 1 Introduction  
The aquaculture industry faces a major challenge of increasing mortality rates in farmed fish. Certain factors seem to affect the death ratio in fish populations. Factors like pathogens, the denormalization of ecosystems, and climate shifts contribute to the increased rates. Marine heatwaves seriously threaten marine aquaculture's sustainability, demonstrating that addressing this challenge is more than ever essential [1;2]. Moreover, pollution and pathogens from human, industrial, and agricultural activities have initiated water quality degradation, which has affected fish populations [3]. Unsustainable practices in the aquaculture industry, such as overstocking, degraded water quality, and excessive antibiotic use, can initiate diseases and cause an incremental increase in mortality rates. The rapid evolution of the aquaculture industry, supported by ecosystem degradation and disease outbreaks, is the main cause of a serious increase in fish mortality rates [4;5]. Existing studies in aquaculture have explored several approaches for addressing fish mortality risks, including digital twin technologies and real-time monitoring systems integrating IoT devices with cloud technology. While these systems aid in early detection and provide insights into the environmental conditions affecting fish health, limitations remain. Prior research often focuses on single factor analyses or small datasets, which limit predictive accuracy and scalability in complex, real-world scenarios. Additionally, traditional modeling approaches, such as Gaussian models or multivariate anomaly detection, may lack the flexibility needed for high-dimensional and non-linear data in aquaculture settings [4;5]. This study combines artificial intelligence (AI) models and agricultural sciences into a project aiming to optimize caged fish farming by introducing innovative technologies and best practices towards sustainability. This project aims to build the foundations of an advanced system for fish disease diagnosis and treatment, aiming to increase the competitiveness of Greek aquaculture. This research proposal introduces a data-driven approach that evaluates machine learning (ML) techniques--specifically Decision Trees (DTs), Random Forest, and Linear Regression--for classifying instances of fish mortality and providing actionable insights. This study overcomes previous limitations by addressing scalability and complexity by integrating these models with a scalable data storage solution (Apache Cassandra) and a distributed processing framework (Apache Spark). Key performance metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R\({}^{2}\)), and the Concordance Index (C-index), are used to assess model accuracy regarding mortality prediction and factor impact. The research question centers on whether a data-driven ML approach can accurately predict Mediterranean-farmed fish species' mortality rates and identify key factors, enabling more effective management and sustainable practices in aquaculture. The model computes various factors, including locations, husbandry methods, water quality, weather conditions, and biological features. The identified thresholds operate as class value ranges, enabling the segmentation of the results into specific classes. The project aims to help through insights into sustainable aquaculture management, offering a proactive mechanism to address emerging fish mortality issues. The findings can lead to the development of targeted survival practices, enabling stakeholders to support caged-farmed fish populations.