In this study, an artificial neural network (ANN) is developed to find the correlation between process parameters and weld bead characteristics of dissimilar joining of Al6061 and A588K high-strength low-alloy (HSLA) steel. The investigation first focused on the feasibility of joining Al6061 and A588K, with an emphasis on examining the bead appearance and morphological characteristics across a range of laser power settings. The results indicated that all welds displayed excellent surface finish and weld bead appearance, with plastically deformed steel particles dispersed at the Al/steel interface. A greater number of alloying elements are dissipated in the fusion zone (FZ), while other alloying elements are found predominantly in the surrounding zones. Finally, the relationships between the process parameters and weld bead characteristics of dissimilar joining of Al6061 and A588K HSLA steel were nonlinear and could potentially be predicted by the ANN models (R2 > 0.999). Experimental results confirmed that the machining model is suitable and the optimization strategy satisfies practical requirements. The model has proven to be both unique and robust, offering flexibility and precision for future applications. © 2026 selection and editorial matter, K. Sripriyan and J. Paulo Davim; individual chapters, the contributors.