A facile and environmentally mindful approach for the synthesis of MoSe 2 QDs was developed via the hydrothermal method from bulk MoSe 2 . In this, the exfoliation of MoSe 2 was enhanced with the aid of an intercalation agent (KOH), which could reduce the exfoliation time and increase the exfoliation efficiency to form MoSe 2 QDs. We found that MoSe 2 QDs display blue emission that is suitable for different applications. This fluorescence property of MoSe 2 QDs was harnessed to fabricate a dual-modal sensor for the detection of both vitamin B 12 (VB 12 ) and vitamin B 9 (VB 9 ), employing fluorescence quenching. We performed a detailed study on the fluorescence quenching mechanism of both analytes. The predominant quenching mechanism for VB 12 is via Forster resonance energy transfer. In contrast, the recognition of VB 9 primarily relies on the inner filter effect. We applied an emerging and captivating approach to pattern recognition, the deep-learning method, which enables machines to "learn" patterns through training, eliminating the need for explicit programming of recognition methods. This attribute endows deep-learning with immense potential in the realm of sensing data analysis. Here, analyzing the array-based sensing data, the deep-learning technique, "convolution neural networks", has achieved 93% accuracy in determining the contribution of VB 12 and VB 9 .