Our study aimed to evaluate the survival impact of adjuvant radiotherapy (RT) following breast-conserving surgery (BCS) in elderly male patients with early-stage, low-risk breast cancer (node-negative, HR+), and to identify RT-benefiting subgroups using machine learning and causal inference approaches. We conducted a retrospective cohort study using Surveillance, Epidemiology, and End Results (SEER) database (2000-2021), including 360 patients after propensity score matching (PSM). Patients were grouped by RT and non-RT (NRT) status, and a 1:3 nearest neighbor PSM was applied. Overall survival (OS), relative survival (RS), standardized mortality ratio (SMR), and transformed Cox regression were used to estimate RT benefit. Additionally, machine learning models, including random forest, support vector machines and causal forest model, were applied for survival prediction and validation. In early-stage, low-risk male breast cancer (MBC) patients treated with BCS, adjuvant RT did not demonstrate a significant survival advantage over NRT. After PSM, 15-year OS, RS, and SMR were 31.8%, 15.2%, and 2.14 for RT versus 34.1%, 21.5%, and 2.25 for NRT (p = 0.36, 0.68, and 0.81, respectively). The cumulative incidence of breast cancer-related death (BCRD) and non-BCRD also showed no statistically significant differences between groups (p = 0.06 and 0.75). Machine learning models (Cox, GBM, and XGBoost) confirmed the limited contribution of RT to survival prediction, with the Cox model demonstrating the best discrimination (C-index = 0.713). While RT was associated with a lower risk of death within the first 10 years, its benefit diminished over time. Causal forest analysis revealed notable heterogeneity in treatment effects across subgroups. Patients who were younger, diagnosed earlier, or had stage I disease showed relatively higher estimated benefit from RT, while older patients or those with more recent diagnoses demonstrated attenuated benefit. In elderly, low-risk MBC patients treated with BCS, adjuvant RT was not associated with improved long-term survival. While our findings suggest that RT may be safely omitted in selected individuals, this decision should be made cautiously in the absence of recurrence data. Model-based analyses underscore the importance of tailoring treatment to patient-specific risk profiles. Prospective studies dedicated to MBC are needed to support individualized de-escalation strategies.