The value of life insurance in enhancing social welfare, ensuring household financial security, and deepening financial markets is well established in the economic literature. In line with the strategic policies of Iran Insurance Company to expand the share of life insurance and move toward data-driven decision-making, identifying the key determinants of life insurance sales has become a strategic priority. Given that the relationships between economic variables and policyholder behavior are not necessarily linear, this study aims to develop a model for life insurance sales growth in Iran Insurance Company using an artificial neural network approach. The data were extracted from the company’s informational dashboard along with official macroeconomic indicators of Iran, and the variables were classified into three categories: individual factors (IF), insurance market factors (BF), and macroeconomic factors (MF). A multilayer perceptron neural network was developed and its performance was compared with multiple linear regression. The results indicate that the neural network achieved a higher coefficient of determination (0.5084) and lower prediction errors, confirming the nonlinear nature of relationships in Iran’s life insurance market. Olden’s importance analysis revealed that macroeconomic factors accounted for 64.27% of total influence. Among the variables, life insurance penetration rate, unemployment rate, and life expectancy showed the strongest positive effects. The findings highlight the necessity of aligning Iran Insurance Company’s sales development strategies with macroeconomic dynamics and adopting intelligent analytical tools.