Chronic Coronary Syndrome (CCS) is a common condition associated with an elevated risk of Major Adverse Cardiac Events (MACEs), such as myocardial infarction, cardiovascular death, and urgent revascularization. Early identification of clinical and paraclinical predictors is essential for risk stratification and secondary prevention. This study aimed to identify independent clinical, biochemical, and imaging predictors associated with the risk of MACEs in patients diagnosed with CCS. We conducted a retrospective cohort study including 132 patients diagnosed with CCS between 2023 and 2025. Clinical characteristics, laboratory data, imaging results, and electrocardiographic findings were analyzed. Univariate and multivariate Cox regression models were employed to assess risk factors. A random forest algorithm was used to evaluate variable importance. The predictive model’s performance was assessed using Harrell’s C-index. During a median follow-up of 2.2 years, 16% of patients experienced MACEs. Independent predictors included diabetes mellitus (HR 1.92; 95% CI 1.45–2.54), reduced left ventricular ejection fraction (LVEF < 40%) (HR 2.15; 95% CI 1.63–2.84), and multivessel coronary artery disease (HR 1.89; 95% CI 1.41–2.53). The predictive model achieved a C-index of 0.78. Diabetes mellitus, reduced LVEF, and multivessel coronary artery disease are strong, independent predictors of MACEs in patients with CCS. These findings support the integration of clinical and imaging data into personalized risk stratification tools to improve secondary prevention.