<p>Vehicular Ad Hoc Networks (VANETs) are networks where vehicles can share important data such as traffic, events, and alerts for safety. However, one of the main challenges in VANETs is secure and efficient data delivery due to the need for reliable next-hop selection. Existing routing protocols struggle to deliver reliable next-hop selection because routing in VANETs is initially uncertain and an all-time real-time environment. In this research, develop a Multi-Criteria Decision-Making (MCDM) model that can perform secure and efficient next-hop selection in VANET routing protocols while considering security and network performance. The proposed MCDM model follows a step-by-step process to collect data on vehicle movement and network metrics (such as including mobility, network quality, and trust indicators). Data preprocessing utilizing Z-score normalization for outlier detection, followed by extracting features using Short-Time Fourier Transform (STFT). The Fuzzy Flying Fox Optimization (FFFO) combines Fuzzy Logic (FL)with Flying Fox Optimization (FFO) to improve next-hop selection in VANET routingprotocols.The goal of FFFO is to efficiently handle uncertainty and dynamic networkconditions by utilizing FL for decision-making and FFO to improve the routing process, resulting in secure, effective, and reliable data transmission in VANETs. The research, using MATLAB and performance indicators like packet delivery delay (PDD) (0.5) for 50 nodes and average packet delivery ratio (PDR) (100) for 200 nodes, found that FFFO outperforms conventional routing algorithms, enhancing efficiency and security. The research demonstrates that using MCDM and FL improves VANET routingperformance, particularly security and dependability.</p>