This research proposes an innovative methodology for estimating coefficients in nonlinear semi-bilinear time series models. We develop a robust estimation framework based on an alternative approach using empirical moments, specifically designed to overcome the limitations of classical methods in the face of the complexity of these hybrid structures. The objective is to provide an efficient, accurate, and computationally viable inference process for these models, where a linear autoregressive component and a nonlinear bilinear operator dynamically interact. Extensive numerical simulations rigorously validate the performance and robustness of our approach. The results demonstrate a marked superiority in terms of bias, variance, and stability of the proposed estimators, compared to conventional estimation techniques, particularly in the context of small samples or pronounced nonlinearities. This methodological advance opens promising application perspectives for the analysis of complex sequential data in demanding fields such as financial markets, economic forecasting, and big data analytics. A complementary and original investigation extends this contribution by examining the critical influence of the specification of the innovation term. A systematic study is conducted by substituting the standard white noise assumption with more realistic and volatile noise structures, such as ARCH, GARCH, COGARCH, and PAR (Periodic Autoregressive Process). This analysis reveals and quantifies the significant impact of the noise distribution, heteroscedasticity, and correlation dynamics on the performance and final accuracy of the estimators, particularly when calibrated using deep learning algorithms. These results underscore the crucial importance of appropriate innovation modeling for optimizing inference and defining the practical conditions for the optimal application of our proposed method.