Risk factors for Type 2 Diabetes may vary significantly between genders, emphasizing the need for gender-specific studies to enhance understanding and management of the disease. This study examines critical contributors to diabetes risk through Bayesian variable selection with Markov Chain Monte Carlo techniques used for model estimation, applied to a cross-sectional sample of 496 patients from the outpatient department of Belle Vue Clinic in Kolkata, India. Using logistic regression models, key factors were evaluated based on lifestyle activities, essential hypertension, body fat percentage, BMI, body age, waist-to-height ratio, and basal metabolic rate (BMR), analyzed separately for males and females. Findings indicate a strong association between diabetes risk and body age in both genders, while BMI significantly impacts risk in males, and BMR is a notable factor among females. The consistency of these models emphasizes the reliability of Bayesian variable selection in epidemiological studies with smaller datasets. Results highlight gender-specific differences in risk factors, providing nuanced insights that align with, and extend, existing literature. This research underscores the potential of advanced Bayesian modeling to inform more targeted public health strategies and intervention programs for Type 2 Diabetes prevention.