<![CDATA[This research also aims to explore how various parameter optimization techniques influence the effectiveness of such models in a realistic academic environment. A literature review in research on optimizing neural networks for predicting student success involves collecting and analyzing literature on data analysis techniques and neural networks in education. This process uses major academic databases to find relevant and significant sources, which are then integrated to define research gaps and formulate research questions. Analysis of the literature review resulted in an in-depth understanding of existing methodologies and identification of trends, as well as revealing gaps in existing research that this study sought to address. Critiques of existing literature also inform the development of more robust and innovative approaches to using neural networks in academic prediction. The results of this work optimize neural networks for predicting student success overcoming challenges such as inconsistent data and overfitting through advanced data preprocessing techniques and model regulation such as dropout and early stopping. The use of cross-validation helps objectively validate the effectiveness of the model. The application of neural network capacity theory and the bias-variance tradeoff principle ensures that the model can generalize well to new data. The results of this research provide a strong basis for data-based educational policy making, increasing the likelihood of student success. The implementation of this neural network model shows broad potential for improving educational processes through predictive technology.]]>
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