Chronic Kidney Disease (CKD) significantly increases the risk of coronary artery disease due to chronic kidney disease specific mechanisms such as uremic toxin accumulation, endothelial dysfunction, vascular calcification, and chronic inflammation, as well as common risk factors like hypertension, diabetes, and dyslipidemia. Although early diagnosis of coronary artery disease (CAD)is essential to bettering heart condition and lowering fatalities in individuals with chronic renal disease, traditional methods for diagnosis frequently fall short in this regard. The research presented here suggests using an advanced machine learning-based methodology to perform early CAD risk identification in individuals with chronic renal disease in order to solve this issue. A clinical dataset comprising 62 health variables was collected from Akash Hospital, Dwarka, New Delhi, India. It constitutes of broad age range and a balanced gender distribution that was used to train and verify the model. Feature selection was performed using a novel hybrid optimization framework that combines Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to extract the most relevant predictors. The following metrics: accuracy, precision, recall, and F1 Score have been employed as measures of performance to assess ten machine learning models, which included simple classifiers to intricate ensemble approaches. The best results were obtained by the suggested PSO improved Support Vector Machine (SVM), which had a success rate of 98.72%, consistency of 0.99, recalls of 0.99, and the F1-score of 0.99. The results obtained show an important advancement beyond the conventional method. The present study offers a greater accuracy in predicting than earlier published methods by systematically integrating hybridization methods for choosing features experiencing machine learning algorithms for CAD risk classification targeted for individuals with chronic renal disease. This framework demonstrates the potential of AI to transform early coronary artery disease detection strategies in high-risk populations.