<p>Fault control in Network Function Virtualization (NFV) is an intelligent model to enhance continuous service accessibility and dependability by detecting and solving faults across distributed cloud systems. It protects optimal performance and correlated NFV infrastructures by employing proactive observing, automatic recovery processes, and replacement alternatives. This work aims to provide a novel fault control system for multi-cloud NFV supported by machine learning (ML). We intend to analyze Fault, Configuration, and performance (FCP) problems categorize severity to moderate service impact, and optimize multi-cloud utilization. We are leveraging modern technologies to analyze complex correlations and predict failures, enhancing diagnostic accuracy and system effectiveness. We propose an innovative Honey-optimized Gaussian-kernel Support Vector Machine (HBO-GK-SVM) to accurately detect errors and faults that arise in a multi-cloud NFV environment. This study obtained the Telstra Competition Dataset from Kaggle, including the severity of network faults based on time and location. The findings analysis phase was done with various parameters including recall, precision, and AUC curve. We also conducted a comparison assessment with other existing approaches to examine the effectiveness of the suggested framework. The experimental findings demonstrated the proposed approach yielded better outcomes in terms of recall of 96%, precision of 95%, and AUC score of 99.2% than other conventional approaches for detecting faults to optimize efficiency in multi-cloud NFV.</p>