Proactive risk management is crucial in the ever-changing field of cybersecurity to prevent advanced threats. This study presents an innovative approach to improve cybersecurity decision-making systems by combining fuzzy logic and Random Forest, resulting in an exceptional accuracy of 98.33%. The hybrid approach aims to tackle the challenges of uncertainty and imprecision in cybersecurity data by offering a clear and strong decision-making framework. The beginning of this paper highlights the increasing cybersecurity threats and the shortcomings of conventional risk management methods. The integration of advanced decision systems is crucial as the paradigm moves toward proactive strategies. Fuzzy logic, known for its capability to manage imprecise data, is combined with the ensemble learning strength of Random Forest to form a thorough and efficient solution. The novel hybrid approach starts by converting input features into fuzzy values to represent the inherent uncertainty in cybersecurity data. A fuzzy inference system is used to convert fuzzy inputs into precise decisions, which serve as the basis for training the Random Forest model. The hybrid system effectively utilizes the ensemble features of Random Forest to identify intricate patterns in the data, resulting in an impressive accuracy of 98.33%. This research enhances cybersecurity decision systems and highlights the interpretability of the hybrid model. The system combines the clarity of fuzzy logic with the strength of Random Forest to provide a transparent view of decision-making processes and maintain a high level of predictive precision. The results highlight the efficacy of the hybrid approach in proactive cybersecurity risk management, showing its ability to greatly enhance organizational defenses against changing cyber threats.