This study looks at the forecasting of GDP and unemployment growth rates in India from 1991 to 2021. First, we employed correlation techniques to investigate the relationship between various economic indicators and GDP. After researching their associations, we discovered highly correlated variables, namely Gross Savings, which was then fitted using multiple linear regression approaches. R2 levels are observed to be extremely low. Second, we applied several nonlinear regression model approaches and discovered that diverse nonlinear models were fitted with the highest R2. Similarly, another important variable unemployment growth rate used correlation technique with various economic indicators. We examined more nonlinear models include polynomial regression, exponential growth models, and followed validation procedures to ensure reliability. Performance was assessed using standard metrics such as Error Sum of Squares (SSE), Mean Squared Error (MSE), Root Mean Sum of Squares (RMSE), and R-squared. We compared and demonstrated the usefulness of nonlinear techniques in improving forecasting accuracy and reflecting the intricacies of India’s economic data in Tables 2 and 3. Finally, it was discovered that the Mechanistic Growth Model is the most successful for forecasting GDP based on Gross Savings, but the Weibull Growth Model is the best-performing model for forecasting unemployment based on Gross Savings, with the highest R2 and the lowest SSE, MSE, and RMSE.