Intuitionistic fuzzy soft sets (IFSSs) are employed to model real-life problems. In IFSS, a parameter reflecting the expert opinion regarding the purity of the information is added to generalize the classical concept of IFSS. The reliability of this generalized notion of IFSS in dealing with decision-making problems more accurately is high than the classical IFSS. The similarity between two generalized intuitionistic fuzzy soft sets (GIFSSs) is calculated by similarity measure. Sugeno integral gives an operation that is similar to the expected value, so it would be an effective tool to determine the expected total similarity degree between two GIFSSs. Hence, we have proposed a novel similarity measure based on the Sugeno integral of GIFSS. We have also studied some of the mathematical properties of our novel similarity measure. Along with the effect of the generalization parameter in the proposed similarity measure, its performance analysis over a decision-making problem discusses the superiority of the proposed similarity measure.