This study evaluates whether an AI-based cardiovascular risk score (AICVD) can more accurately predict 10-year cardiovascular events than conventional tools in Indian and external populations. The authors retrospectively assembled 31,599 adult preventive health-check records from six Apollo Hospitals (2009–2018), selected 14–21 predictors via correlation thresholds and propensity score matching, and built both a Cox proportional hazards model and a deep-learning survival model; they then validated performance on independent Indian cohorts (Delhi, Kolkata) and an external Netherlands cohort (MUMC), comparing against Framingham Heart Risk Score (FHRS) and QRisk3 using AUC, precision/recall, F1, likelihood ratios, and calibration. The deep-learning hazards model achieved AUC 0.853 in development, with validation AUCs ≈0.84 (Delhi) and ≈0.92 (Kolkata), and outperformed FHRS and QRisk3 on accuracy and positive likelihood ratios; in the MUMC cohort, AICVD also exceeded FHRS (AUC 0.737 vs 0.707) though with slight overestimation at higher risk. Key risk contributors included diabetes (HR ≈2.34), hypertension (≈1.54), smoking (≈2.28), chewing tobacco (≈2.01), DBP (≈1.07), and dyslipidemia (≈1.16), with precision–recall analysis favoring the deep model for identifying high-risk individuals; combining deep learning with Cox improved discrimination and interpretability. The discussion highlights that conventional scores underperform across geographies/ethnicities, whereas AICVD—incorporating lifestyle and vitals—generalizes better in Indian settings and reasonably to Europe, while acknowledging limits of retrospective design, some missing predictors in external data, population differences, and the need for prospective, multicenter recalibration.