This paper presents an updated review of preventive analytics techniques for cardio and diabetic disease prediction using machine learning. We examine feature selection methods (filter, wrapper, embedded, hybrid) and six categories of machine learning models – Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NN), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), and Logistic Regression (LR) – across a broad dataset of studies. A total of 40 relevant research works are analyzed to evaluate model performance in diverse data environments, ranging from large public health cohorts to clinical records. Unlike previous reviews that primarily summarize algorithms or datasets, this paper applies a meta-analytic synthesis framework to systematically compare study outcomes, highlight cross-study performance patterns, and quantify which techniques emerge as “most usable” versus “most accurate.” Consistent with prior findings, ensemble tree-based models such as Random Forest often achieve high accuracy for diabetes prediction (reported up to ~84% in some studies), while SVM remains one of the most commonly utilized algorithms. For cardiovascular disease, Artificial Neural Networks and deep learning approaches show competitive performance, with one study reporting ~71% accuracy using an ANN on large-scale data. Current works also show that advanced methods (e.g., gradient boosting and hybrid deep learning frameworks) can outperform traditional models in sensitivity and AUC for heart disease prediction, sometimes exceeding 90% accuracy on benchmark datasets. By integrating results across heterogeneous datasets using a meta-analytic lens, this review provides a consolidated comparative assessment that clarifies algorithmic strengths and informs future preventive analytics modeling.