This research investigates the application of neuromorphic computing-based neural networks for optimizing machine-learning techniques in the context of Industry 4.0. Through ten trials of quantitative performance evaluation, the average accuracy of the proposed model reached 88.2%, showcasing its adeptness in making precise predictions. Precision, recall, and F1 score exhibited strong values, averaging at 90.2%, 85.3%, and 87.8%, respectively. These results emphasize the model's efficacy in balancing precision and recall, essential for robust industrial applications. The scalability analysis further substantiates the model's reliability under varying data volumes and computational demands. With execution times measured in milliseconds, the model demonstrated consistent and manageable response times across different scales. Notably, even under very high computational demands, the model maintained low execution times, indicating its adaptability to the dynamic and resource-intensive nature of Industry 4.0 environments. These numerical outcomes underscore the viability and practicality of integrating neuromorphic computing-based neural networks into Industry 4.0 processes. The achieved accuracy, precision, recall, F1 score, and scalability values provide concrete evidence of the model's performance, laying a solid foundation for its potential application in real-world industrial scenarios. This research contributes valuable insights to the ongoing discourse on intelligent and adaptive systems, marking a significant stride toward the realization of optimized machine-learning techniques in the Industry 4.0 landscape.