Diabetic cardiomyopathy (DCM) results from prolonged impairment of cardiac gluco-lipid metabolism in diabetes leading to increased mitochondrial dysfunction, oxidative stress, inflammation, cardiac remodeling, and heart failure. The cardiometabolomes involved are used as diagnostic, prognostic, and treatment biomarkers complimentary to the imaging technologies such as echocardiography. However, the imaging readouts can only belatedly detect structural cardiac malfunctions. Artificial Intelligence (AI) is a novel tool in healthcare technology that is currently partially applied to DCM staging, following digitization of the imaging data readouts but not to the cardiometabolites data, which suffer lower sensitivity, specificity and accuracy, despite their advantage in sub-clinical disease diagnosis. We searched online databases for articles published in English between 2020 and 2024 on DCM using appropriate search words. The mean values of global pooled data on glycosylated hemoglobin (HBA1c), Brain-type Natriuretic Peptide (BNP), cardiac Troponin I (cTnI) were determined by Bayesian hierarchical meta-regression models and the Area-Under-the-Curves that were calculated from the constructed Receiver Operating Characteristic curves at 70% sensitivity were used to predict the specificity of the metabolites sensing compared to the standard reference, respectively. The HBA1c, BNP and cTnI predictions were 92%, 80% and 40%, respectively, compared to the standard DCM diagnostic criteria. These figures can be up-scaled, digitized, and digitilized in AI empowered algorithms to predict DCM pathogenesis at the sub-clinical stage. Though diagnostically inferior to the commonly used imaging techniques, AI can be plugged into cardiometabolome sensing to mitigate the development of DM at pre-clinical stage.