NMR spectroscopy is an incredibly powerful analytical tool that provides a wide array of techniques for conducting metabolomics studies. It has become an essential tool in for assessing quality, authenticity, safety, and geographical origin. However, several challenges hinder its broader application in food research, such as low sensitivity, signal overlap, and the predominance of sugar signals that obscure important signals of non-sugars. To overcome this limitation, we report the implementation of past reported sugar-suppression methods combined with these conventional standard 1D and 2D NMR methods. This approach minimizes the sugar signals and allows for clearer detection of other metabolites. Total sugar and non-sugar sixty-eight metabolites were identified, and three-fold enhanced intensities and visibility of non-sugar signals in suppressed 1D 1H NMR and 2D NMR methods compared to their respective standard  NMR methods. Combining NMR-based metabolomics with multivariate analyses like PLS-DA and heat maps helps identify compositional variations in honey samples, aiding in the discrimination of botanical origins. . Glucose, cellobiose, fructose, galactose, glucose-6-phosphate, maltose, mannitol, ribose, sucrose, trehalose, uridine diphosphate (UDP)-galactose, UDP-glucose, adenosine diphosphate (ADP), adenosine monophosphate (AMP), adenosine triphosphate (ATP), acetate, asparagine, betaine, formate, gallate, glutamate, glutamine, malonate, hydroxymethylfurfural, phenylalanine, pipecolate, and AA1 proline are main responsible primary metabolites that contributed to botanical origins discrimination. Interestingly, the applied suppressed methods improved signal intensity, enabling clearer identification of metabolites amidst complex carbohydrate backgrounds. This approach also facilitated the detection of vitamins like nicotinic acid and trigonelline, emphasizing the nutritional value of the honey samples by reducing interference from dominant carbohydrate signals.