The current research focused on extraction optimization of bioactive compounds from Strychnos potatorum seeds (SPs) using an eco-friendly glycerol-sodium acetate based deep eutectic solvent (DES). The optimization was accomplished using response surface methodology (RSM) and artificial neural networking (ANN). The independent variables included shaking time (A), temperature (B), and solvent-to-feed ratio (C), and the responses were the extraction yield, total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (DPPH), and antidiabetic activity (α-amylase inhibitory activity). The SPs extracts obtained under optimal conditions (29 min, 40 °C and 30 mL/g of A, B, and C parameters, respectively) had 30.43 mg gallic acid equivalents (GAE)/g of dry weight (DW) TPC, 10.99 mg rutin equivalents (RE)/g DW TFC, 26.16 % antioxidant activity and 46.95 % α-amylase inhibitory activity. For all the outputs, the ANN percentage error was less than the RSM percentage error for the predicted values against the experimentally measured values. The results were further supported by the %AAD (% absolute average deviation) and R