AbstractFormulation of reduced‐calorie, antioxidant‐enriched sapodilla‐based spread was developed using rotatable central composite design of factors/ingredient proportions (mass ratio of sapodilla pulp to grape juice, pectin, and citric acid) for responses (total soluble solids, moisture, phenolics, antioxidant capacity, and firmness). Response surface methodology (RSM) and artificial neural network (ANN) were combined with method of steepest ascent (SA) and genetic algorithms (GA) for optimization, leading to hybrid techniques: RSM‐SA, RSM‐GA, and ANN‐GA. RSM modeling proved statistically more efficient than ANN modeling. Fuzzy logic analysis of RSM‐SA, RSM‐GA, and control (sapodilla spread without grape juice) samples revealed RSM‐SA (sapodilla to grape ratio = 1.14; pectin 0.58 g/100 g; citric acid 0.21 g/100 g with TSS of 29 °Bx, 65% moisture; 11.44 g GAE kg−1 phenolics, 83.2 mg GAE kg−1 antioxidant capacity, and firmness of 47.3 N) had highest sensory acceptability. Its total sugars and antioxidant activity were 0.88 and 1.5 times those of control sample.Practical ApplicationsFood products are conventionally formulated using the cook‐and‐look approach. With the rising consumer awareness regarding healthy foods, it has become essential for food industries to develop food formulations with enhanced nutritional value and quality attributes. The use of computational models helps in studying the effects of ingredients on the characteristics of food products. Further, such models can be used to determine the combination of ingredients that maximize the nutritional value and quality of the food product. Such a systematic approach in formulating food products helps in saving time, resources, and efforts. In this study, a reduced‐calorie, antioxidant‐rich sapodilla‐based spread has been formulated.