BACKGROUND: Machine learning (ML) integration of clinical, metabolite, and genetic data reveals variable results in predicting cardiometabolic health (CMH) outcomes. Therefore, we aim to (1) evaluate whether a multi-modal approach incorporating all three data types using ML algorithms can improve CMH outcome prediction compared to single-modal or paired-modal models, and (2) compare the methodologies used in existing prediction models. METHODS: We systematically searched five databases from 1998 to 2024 for ML predictive modelling studies using the multi-modal approach for CMH outcomes. Risk-of-bias assessment tools were used to assess methodological quality. Study characteristics, ML algorithms, data preprocessing, evaluation methods and metrics, feature selections, and feature importance parameters were synthesized narratively to show methodological heterogeneity. RESULTS: Of the four included studies (3 ML algorithms), three were at low risk of bias, and one was at high risk. The multi-modal approach consistently improved T2D and BP prediction compared to single-modal or paired-modal models. Genetics showed the lowest predictive performance in three studies. Logistic regression (n = 2 studies) and random forest (n = 1) were used in T2D studies, while XGBoost was used in one BP study. One study with missing data and variations in feature selection across all studies hindered a comprehensive comparison of feature importance. CONCLUSIONS: Our review emphasizes the potential improvement in T2D and BP prediction using ML algorithms with the multi-modal approach. However, further studies using diverse ML algorithms with optimized methodologies on single-modal, paired-modal, and multi-modal models are needed to gain insights into biomarker selection for predicting CMH outcomes.