This study analyzes the synthetic control method (SCM) and asserts that weight restrictions limit the model’s flexibility and predictive ability. Additionally, this research experimentally explored alternative loss functions such as mean absolute error and regularization methods, such as LASSO or ridge regression, to improve model generalization and reduce overfitting within SCM applications. Moreover, the study proposes methodologies for computing confidence intervals, such as bootstrap sampling and quantile regression, to advance SCM the development. Furthermore, this study highlights the overlooked issue of residual autocorrelation in SCM research. It emphasizes the importance of effectively managing autocorrelation issues in the SCM by applying a time-series model and conducting the Durbin-Watson test. This study aimed to provide more accurate and reliable inferences, deepen our understanding of the SCM, and expand its application to the fields of data science and policy analysis.