G protein-coupled receptors (GPCRs) are critical membrane proteins involved in a broad range of physiological functions and are frequently associated with aging-related diseases. Traditional phylogenetic methods such as multiple sequence alignment (MSA) and distance-based tree construction capture evolutionary signals primarily through sequence similarity. In contrast, recent advances in protein language models (PLMs) offer a novel means of encoding protein features via embeddings that integrate structural and contextual information. In this study, we investigate the phylogenetic and functional landscape of 14 aging-associated Class A GPCRs using both traditional alignment-based methods (NJ and UPGMA) and ESM-2-derived embeddings. We construct and compare three phylogenetic trees, perform principal component analysis (PCA) to identify latent structural outliers, and analyze cosine similarity networks to uncover connectivity patterns. The resulting divergence, measured via Robinson-Foulds distances, reveals that embedding-based methods capture complementary, functionally relevant relationships overlooked by classical approaches. Our findings highlight the potential of PLMs in evolutionary bioinformatics and provide a reproducible framework for embedding-driven protein family analysis.