Normalization is a critical pre-processing step for Neural Networks, especially when dealing with datasets that have features with different ranges. It standardizes the data, facilitating faster convergence of the optimization algorithm and preventing it from being trapped in suboptimal local minima. Techniques like min-max normalization or z-score normalization can be applied to ensure uniformity across all features, thereby enhancing the model's ability to learn the relationships between features and the target variable effectively. This approach is both cost-effective and timely, aligning with the project's constraints.