Given the ongoing evolution of cloud computing platforms and the increasing complexity of cyberattacks, risk assessment is a critical topic. By utilizing algorithmic modeling to forecast risks and altering the traditional Virtual Nominal Group Technique (VNGT), the current study offers an improved method for risk evaluations. The suggested method uses data analysis tools to categorize worry levels, assess possible risks, and offer useful information for proactive risk minimization. The approach enhances cloud security decisions by combining measurable predictive machine learning models with expert-driven subjective assessments. A variety of machine learning algorithms, including supervised and unsupervised methods, are also examined in order to improve the accuracy of risk prediction. Validated on real-world cloud security datasets, the methodology's application shows how well it enhances recognizing risks and remediation tactics.