In this study, a class of exponential ratio-type estimators is proposed for the estimation of the population mean under simple random sampling without replacement. These estimators are specifically designed to enhance estimation efficiency by incorporating supplementary information from both continuous and categorical variables. The effective utilization of such information plays a significant role in improving the precision of estimates for finite population parameters, including the mean, proportion, total, and variance. To establish their theoretical properties, expressions for the bias and mean squared error(MSE) are derived up to the first order of approximation. In order to assess their empirical performance, a comprehensive numerical analysis is conducted using five real-world datasets, with results compared against several well-established estimators. Following this, the proposed class of exponential ratio- type estimators for the estimation of the population mean under simple random sampling without replacement is presented in detail, including theoretical derivations and properties. Finally, the study concludes by summarizing the key findings, emphasizing the superiority of the proposed estimators in terms of efficiency, and offering recommendations for future research and practical applications.