Diabetes is among the current most important global health crises. With prevalence increasing at a very rapid rate, there is a need for inventive predictive solutions. ML has emerged to hold great promise in offering early detection, risk stratification, and management based on diverse data sources for dealing with diabetes. This paper identifies how hybrid ML models develop and improve diabetes prediction outcomes. It synthesizes findings, discusses methodological underpinnings, and teases out key findings from relatively recent research. Hybrid models that integrate clinical, genetic, and lifestyle data show better performance in predictive accuracy, interpretability, and clinical utility. Further, challenges regarding heterogeneity in data, model explainability, and ethical challenges have also been discussed in order to present the future direction of research and clinical translation.