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With the increasing demand for high-quality regional economic development, accurately extracting hidden features of economic data and achieving reliable development trend prediction has become an important prerequisite for formulating scientific economic policies. To optimize the feature extraction accuracy of regional economic data and the development prediction reliability, a model for feature extraction and development prediction of regional economic data is constructed by integrating an improved grey wolf optimization algorithm, a support vector machine, and a generative adversarial network. On the F1 unimodal function and F2 multi-modal function tests, the convergence speed was significantly better than that of the wind-driven optimization and sine cosine algorithm, demonstrating stronger adaptability in complex optimization problems. The comparative experiment on the steel wire rope dataset showed that the algorithm improved the recognition rate by 1.25% compared to the Principal Component Analysis-Grey Wolf Optimizer-Support Vector Machine, reaching 98.75%, and had higher efficiency in high-dimensional feature processing, verifying its superiority in feature extraction and classification recognition. The model was applied to the economic data in Anhui Province, selecting 8 core indicators such as the GDP of the primary industry and the income of urban and rural residents from 2011 to 2022. In 2011, when the true value was 16,311, the predicted value of the research model was 16,200. In 2015, when the true value was 23,808, the model predicted a value of 23,600. The minimum absolute error from 2011 to 2020 was only 103, and the error rate was as low as 0.005, demonstrating outstanding stability in medium and long-term forecasting. The proposed model can effectively capture the characteristics of regional economic data, improve prediction accuracy, and provide a scientific basis for regional economic development decisions.