The increasing availability of high-dimensional data in modern applications has introduced significant challenges for neural network performance, including overfitting, high computational complexity, and reduced predictive accuracy. To address these issues, various optimization strategies have been developed to enhance the efficiency and effectiveness of neural networks. The distribution of commonly used optimization techniques, indicating that dimensionality reduction accounts for the largest proportion (25%), followed by regularization, hyperparameter tuning, and ensemble methods (each approximately 20%), while feature selection contributes 15%. The improvement in classification accuracy was achieved through different optimization techniques. The baseline neural network model achieves an accuracy of approximately 0.72, which increases to 0.80 with regularization and 0.83 with dimensionality reduction. Further improvements are observed with hyperparameter tuning (0.86), while the hybrid ensemble approach achieves the highest accuracy of approximately 0.90. A conceptual representation of the optimization pipeline was provided, illustrating how high-dimensional data is transformed into an optimized neural network model through the application of various optimization techniques. This structured approach ensures improved generalization, reduced computational complexity, and enhanced model reliability. Overall, the findings indicate that optimization strategies play a critical role in improving neural network performance in high-dimensional environments. This study highlights the importance of adopting a comprehensive optimization framework to achieve accurate and efficient data-driven decision-making.