Diabetes mellitus (DM) is a metabolic condition with improper regulation of blood sugar levels and is one of the leading global causes of death. Glycated hemoglobin (HbA1c) serves as a crucial indicator for managing diabetes. This study proposes a noninvasive approach to classify glycemic status based on HbA1c levels. This work uses novel Mel-frequency cepstral coefficient features of finger photoplethysmographic (PPG) signals and physiological parameters, enabling straightforward detection of DM. A finger PPG dataset (in reflective mode) comprising 180 subjects with diabetes, prediabetes, and normal HbA1c levels is curated and used to validate the proposed method. The dataset comprises 93 normal (HbA1c 5.7%), 57 prediabetic (HbA1c 5.7%–6.4%), and 30 diabetic (HbA1c 6.5%) individuals. Furthermore, a hybrid feature (HyF) selection method is employed for feature reduction. The HyF Selection-based gradient boosting model achieved effective accuracies of 93.89% for binary classification and 91.67% for multiclass classification. The results are compared with the gold standard HbA1c test. Both the binary and multiclass classifications show improved overall performance. These findings indicate that PPG signals are a feasible substitute for noninvasive HbA1c detection and have potential for wearable HbA1c monitoring.