Black-hole (BH) attacks are among the most critical security threats in Mobile Ad Hoc Networks (MANETs) due to their decentralized and highly dynamic nature. Existing protection mechanisms primarily rely on threshold-based monitoring, trust evaluation, and route observation techniques, which often suffer from limited packet information, poor scalability, and inadequate attack verification capabilities. To address these limitations, this paper proposes a centralized federated framework for black-hole attack detection in MANETs. The proposed framework integrates Linear Scaling-based Shark Smell Optimization Algorithm (LS-SSOA) for Cluster Head (CH) selection, Gini Canberra Spearman-based K-Means Algorithm (GCS-KMA) for clustering, Round Log Sum BCrypt-based Message Authentication Code (RLBC-MAC) for secure route authorization, and Judy-Verkle Tree (JVT) for malicious path verification. A federated learning architecture consisting of a global server and local models is employed to continuously monitor network activities and improve attack detection efficiency. Experimental evaluation was conducted using MANET networks comprising 50–250 nodes in a Python-based simulation environment. The proposed framework achieved a detection accuracy of 98.96%, a false prediction rate of 2.06%, throughput of 6445 kbps, and reduced computational overhead compared with existing methods. The results demonstrate that the proposed centralized federated framework significantly improves network security, route reliability, and attack detection performance in MANET environments.