Breast cancer (BC) continues to be the most common malignancy among women globally. Calmodulin (CaM), a pivotal calcium-binding protein, is implicated in multiple tumor-associated signaling pathways. Here, we aimed to identify diagnostic calmodulin-related genes (CRGs) in BC by integrating bioinformatics and machine learning approaches. Transcriptomic profiles from The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed CRGs (DE-CRGs) by intersecting differentially expressed genes with a curated set of 255 CRGs. Functional enrichment analyses (GO and KEGG) revealed their involvement in key biological processes and pathways. Four machine learning models—LASSO regression, neural networks, random forest, and support vector machine-recursive feature elimination (SVM-RFE)—were employed to prioritize hub genes, and SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability. The diagnostic performance of candidate genes was additionally corroborated in three independent Gene Expression Omnibus (GEO) datasets. A total of 51 DE-CRGs were identified, from which four potential biomarkers (CNN1, IQGAP3, ADD3, and SPTBN1) were selected. Notably, ADD3 and SPTBN1 demonstrated robust diagnostic accuracy across all validation cohorts (AUC > 0.89). SHAP analysis suggested that their elevated expression confers a protective effect, potentially through modulation of cytoskeletal dynamics and calcium signaling pathways. Taken together, our study identifies ADD3 and SPTBN1 as promising diagnostic biomarkers for BC through an explainable AI-driven framework, underscoring their potential utility in precision oncology.