<p>Detecting communities in complex networks is still a difficult task, especially because of the shortcomings of conventional label propagation techniques, which frequently include instability and randomness in label assignment. Moreover, the majority of current methods fail to recognize higher-order structural patterns that are essential for recognizing close-knit communities, including triangular motifs. This paper discusses an innovative hybrid approach to address these problems, named H-LPMC (Hybrid Label Propagation using Motifs and Similarity Measure for Community Detection). It combines similarity-driven label propagation with higher-order motif structures to improve the precision and stability of label-based community detection. It includes the advantages of Stable Label Propagation and Motif-Aware Weighted Label Propagation. Triangle motifs, structurally significant subgraphs, are first identified, and then, a motif-based hypergraph is created, encoding triangle co-occurrence as edge weights. This higher-order structure is then combined with the initial topology to reweight the network. To provide deterministic and stable guidance for label propagation, a similarity measure is calculated, driven by centrality metrics and the common neighbor matrix. Extensive evaluation on several real-world networks shows that H-LPMC outperforms its counterparts in terms of modularity (Q) and normalized mutual information. The suggested methods’ dependability and robustness are further validated by statistical significance testing.</p>