The rise of machine learning, particularly neural networks, has led to their deployment in complex and dynamic environments. However, these models often lack robustness, leading to performance degradation in unforeseen conditions. This study aims to enhance neural network robustness through a novel cross-validation approach that adapts training models to dynamic conditions. We employed a diverse dataset representing varied real-world scenarios and applied a multi-faceted algorithmic technique that dynamically adjusts model parameters during training. Our findings demonstrate a significant increase in model accuracy and robustness by up to 30% in environments that deviated from initial training conditions. Additionally, the method reduced computational overhead, facilitating faster adaptation times. The results underscore the potential of adaptive cross-validation methodologies in maintaining neural network performance across diverse applications. We conclude that this approach offers a scalable solution for deploying machine learning models in dynamic and uncertain environments, paving the way for future research on adaptive learning systems.