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The manufacturing sector increasingly relies on artificial intelligence (AI) to automate fault detection processes. However, existing methods for anomaly detection often struggle with data imbalances and high computational demands, limiting their scalability in industrial environments. This paper implements a system for efficient gearbox anomaly detection in manufacturing processes. We propose a machine learning-based automated system for detecting anomalies in the sound produced by gearbox systems in functional testing facilities. An evaluation of four machine learning (ML) algorithms was performed. Gaussian Mixture Model (GMM), Isolation Forest, K-Means, and One-Class Support Vector Machine (OC-SVM). The OC-SVM algorithm demonstrated the highest level of performance, achieving 88% specificity. This paper also discusses the development and on-site deployment of a fully functional solution that enables real-time fault detection. Our approach reinforces the feasibility of using sound analysis with semi-supervised learning to enhance anomaly detection in industrial settings, bridging the gap between AI-driven quality control and practical deployment. Future work will focus on improving the defective dataset and increasing the interpretability of the prediction model. These objectives are designed to promote improved transparency and trust in the system.