The continuous rise in NCDs exerts pressure on government budgets, health services and personal patient finance. This has led policymakers to implement reforms and predicting models to mitigate the rise of the NCDs. The purpose of this study is to investigate the best model to predict factors influencing NCDs by using both statistical analysis and machine learning (ML) prediction methods. Statistical analysis models such as Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were used to extract the most important information from the dataset and analyze the observations' structure. To improve the accuracy of the statistical model, four optimization algorithms were proposed: genetic algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE) and Ant Colony Optimization (ACO). Two statistical criteria, such as root-mean-squared error (RMSE) and coefficient determination (R2), were used to assess the aforementioned models potential. The results revealed that PLS performed better than PCA, with low RMSE values of 0.41809 and 0.42752 and R2 values of 0.9724 and 0.9737 in both the training and the testing data. Further, to improve the accuracy rate of PLS, hybrid models (GA-PLS, DE-PLS, PSO-PLS and ACO-PLS) is developed. Evaluating the obtained results demonstrated a higher accuracy ability to predict NCDs. The results indicated that the GA-PLS model provided the highest performance in predicting the NCDs. The RMSE and R2 values of (1.235e-3 and 4.643e-3) and (1and 1) were obtained for training and testing data of GA-PLS model, respectively. The most important input parameters in predicting NCDs were identified. This study indicates how statistical analysis and machine learning modeling could help stakeholders make preliminary decisions regarding NCDs.