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Background and Objectives: Physical inactivity is a major modifiable lifestyle factor associated with type 2 diabetes mellitus (T2DM). However, real-world primary care datasets often rely on pragmatic clinical estimates of physical activity (PA) and may lack HbA1c, fasting glucose, dietary data, diabetes duration, and detailed medication information. Therefore, PA-T2DM associations in routine care must be interpreted within the limitations of cross-sectional observational data. Materials and Methods: This cross-sectional observational study analyzed 863 adult patients from a real-world primary care cohort. Participants were categorized into low, moderate, and high PA groups according to self-reported habitual weekly activity levels estimated during routine physician interviews. PA categories were based on clinically meaningful weekly activity thresholds consistent with public health recommendations. The primary outcome was documented T2DM. Secondary variables included lipid profile, blood pressure, body mass index, waist circumference, smoking status, and statin therapy. Multivariable logistic regression models were used to assess associations between PA category and T2DM, with high PA as the reference group. Results: T2DM prevalence showed a graded inverse cross-sectional association across PA categories, being highest in the low PA group (29.7%), intermediate in the moderate PA group (16.7%), and lowest in the high PA group (8.9%) (p < 0.001). In the fully adjusted model, low PA was strongly associated with higher odds of T2DM compared with high PA (odds ratio [OR] 4.32, 95% confidence interval [CI] 2.61-7.15, p < 0.001). Moderate PA was also associated with higher odds of T2DM compared with high PA (OR 2.07, 95% CI 1.23-3.48, p = 0.006). LDL-C differed significantly across PA groups, with the lowest values observed in the high PA group, whereas most other cardiometabolic parameters were comparable. Conclusions: Lower self-reported PA was strongly associated with higher T2DM prevalence after multivariable adjustment in this real-world primary care cohort. These findings support routine PA assessment as part of lifestyle-based cardiometabolic risk stratification. Because dietary intake, HbA1c, diabetes duration, glucose-lowering medication details, and regulatory biomarkers were not available, causal and mechanistic conclusions cannot be drawn. Future studies should integrate PA, nutritional assessment, glycemic markers, medication data, and physiological regulatory measures to better characterize lifestyle-related diabetes risk.