Recently, gender equality and women's entrepreneurship have gained considerable attention in global economic development. Prior to the design of any policy interventions to increase women's entrepreneurship, it is significant to comprehend the factors motivating women to become entrepreneurs. The non-understanding of the factors can result in the endurance of low living standards and the design of expensive and ineffectual policies. But female involvement in entrepreneurship becomes higher in developing economies compared to developed economies. Women Entrepreneurship Index (WEI) plays a vital role in determining the factors that enable the flourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society. Therefore, it is needed to design an automated and accurate WEI prediction model to improve women's entrepreneurship. In this view, this article develops an automated statistical analysis enabled WEI predictive (ASA-WEIP) model. The proposed ASA-WEIP technique aims to effectually determine the WEI. The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing, WEI prediction, and parameter optimization. For the prediction of WEI, the ASA-WEIP technique makes use of the Deep Belief Network (DBN) model, and the parameter optimization process takes place using Squirrel Search Algorithm (SSA). The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repository. The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.