Present study focused on fabrication of heterojunction photodetector based on tin diselenide and multiwalled carbon nanotube. Nanosheets of SnSe2 have been synthesized using two stage process. Firstly, crystals of tin diselenide have been grown using vapour transport technique and nanosheets of SnSe2 are synthesized from liquid phase exfoliation technique from the bulk crystals. SnSe2/CNT hybrid has been prepared in 70–30 wt% ratio and characterized with the preliminary characterization methods. The photodetector is fabricated by depositing the hybrid on ITO substrate and evaluated for its intended application as a photodetector in vacuum condition for different illumination and temperature. Temporal photo-response has been measured for the evaluation of various detection parameters like photo-current, rise time and responsivity. For analysis of photocurrent behavior, a K-Nearest Neighbors (KNN) regression model was employed to predict photo-response parameters as a function of temperature and illumination intensity. The model displayed excellent predictive efficiency (R2 > 0.995) to indicate how well it accounted for the nonlinear photocurrent relation with externalities. Machine learning enables data-based performance analysis with deeper understanding of photocurrent variability, rise time, and responsivity.