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The persistent evolution of cyber threats has given rise to Gen V Multi-Vector Attacks, complex and sophisticated strategies that challenge traditional security measures. This research provides a complete investigation of recent intrusion detection systems designed to mitigate the consequences of Gen V Multi-Vector Attacks. Using the Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we evaluate the efficacy of several different intrusion detection techniques in adjusting to the dynamic nature of sophisticated cyber threats. The study offers an integrated analysis, taking into account criteria such as detection accuracy, adaptability, scalability, resource effect, response time, and automation. Fuzzy AHP is employed to establish priority weights for each factor, reflecting the nuanced nature of security assessments. Subsequently, TOPSIS is employed to rank the intrusion detection methods based on their overall performance. Our findings highlight the importance of behavioral analysis, threat intelligence integration, and dynamic threat modeling in enhancing detection accuracy and adaptability. Furthermore, considerations of resource impact, scalability, and efficient response mechanisms are crucial for sustaining effective defense against Gen V Multi-Vector Attacks. The integrated approach of Fuzzy AHP and TOPSIS presents a strong and adaptable strategy for decision-makers to manage the difficulties of evaluating intrusion detection techniques. This study adds to the ongoing discussion about cybersecurity by providing insights on the positive and negative aspects of existing intrusion detection systems in the context of developing cyber threats. The findings help organizations choose and execute intrusion detection technologies that are not only effective against existing attacks, but also adaptive to future concerns provided by Gen V Multi-Vector Attacks. [ABSTRACT FROM AUTHOR]