The mobile adhoc network (MANET) has attracted considerable attention from researchers due to its dynamic and versatile nature. Graph clustering algorithms can be more effective than optimization algorithms in modeling and analyzing networks because these algorithms arrange nodes into clusters based on their connectivity. According to this context, a graph kernel-based clustering algorithm (GKCA) was developed for MANETs by combining the d-hop graph kernel and clustering scheme. Additionally, it uses the shortest route to connect multiple cluster head (CH) nodes for data transfer. MANETs face challenges such as changes in network structure and disruptions in communication links, which result in an increase in route discovery requests and longer mean end-toend delays (MED) due to longer link reconnect times. Hence, this article proposes the GKCA with link failure prediction (GKCA-LFP) in MANETs to prevent path failures resulting from node mobility. The GKCA is initially used to determine the cluster size and CH nodes. The shortest route is used to connect the CHs for data transfer. Then, the LFP strategy is introduced at this stage to maintain the path. This strategy aims to predict the current link status based on mobility and position information to prevent failure conditions and minimize packet loss ratio (PLR). The GKCA-LFP algorithm can choose more stable shortest paths to connect CHs for data transfer, resulting in decreased MED and PLR. The extensive simulations show that the GKCA-LFP algorithm outperforms the GKCA, AMAC, MARP-HO, and RS-GG algorithms in MANETs. Specifically, for 100 nodes, the GKCA-LFP algorithm achieves a 1.4% control packet ratio (CPR), 0.8% PLR, and 455µs MED. Additionally, for nodes with a mobility speed of 20m/s, the GKCA-LFP algorithm achieves a 1.2% CPR, 2.3% PLR, and 120µs MED.