This study proposes a novel approach to computing the plankton-oxygen dynamics model using deep neural networks (DNNs) within the context of climate change. By leveraging advanced computational methods, particularly deep learning algorithms, we aim to enhance our understanding of how plankton populations and oxygen concentrations interact in response to changing environmental conditions. The integration of DNNs offers several advantages, including the ability to capture complex nonlinear relationships and patterns from large datasets, making them well-suited for modeling dynamic systems such as aquatic ecosystems. By training DNNs on observational data and environmental variables, we can develop predictive models that simulate the behavior of plankton-oxygen dynamics under different climate scenarios. This research builds upon existing studies in ecological modeling and deep learning techniques to advance our knowledge of plankton-oxygen dynamics and their implications for ecosystem resilience in the face of climate change. By computationally modeling these dynamics, we can gain valuable insights into the mechanisms driving ecosystem responses to environmental stressors and inform conservation efforts and policy decisions.