Cardiovascular disease (CVD) continues to represent the primary cause of morbidity and mortality in individuals with chronic kidney disease (CKD), accounting for a significant proportion of premature deaths in this vulnerable population. Patients with CKD are up to 20 times more likely to die from cardiovascular causes than to progress to end-stage renal disease. This elevated risk cannot be fully explained by traditional cardiovascular risk factors alone—such as hypertension, diabetes, dyslipidemia, or smoking—as the pathophysiology of CVD in CKD involves a complex interplay of both traditional and non-traditional mechanisms. These include chronic inflammation, endothelial dysfunction, vascular calcification, oxidative stress, anemia, and disturbances in calcium-phosphorus metabolism. Furthermore, conventional cardiovascular risk prediction models (e.g., Framingham Risk Score) were developed for the general population and often fail to accurately stratify risk in CKD patients. This limitation arises from the exclusion of renal-specific variables such as estimated glomerular filtration rate (eGFR), proteinuria, and markers of systemic inflammation, which have been shown to independently predict cardiovascular outcomes in CKD cohorts. As a result, there is a growing recognition of the need for CKD-specific cardiovascular risk models that account for the unique biological and clinical characteristics of this group. This article presents a cardiovascular risk prediction model specifically tailored for CKD patients, integrating both conventional risk factors and CKD-specific variables (e.g., estimated glomerular filtration rate [eGFR] and inflammation markers. The model was developed and validated using data from a CKD cohort and demonstrated improved accuracy. The study underscores the importance of personalized risk assessment tools in guiding preventive strategies and clinical decision-making for CKD patients at high risk of cardiovascular events.