•Method for optimising pin-in-paste technology was developed.•Multiple machine learning techniques were analysed and compared.•Sensitivity analysis of the applied model was performed.•The first pass yield of the production can be enhanced based on our results.•Managerial implications were also summarized based on the results. Pin-in-paste technology is an advanced and constantly developing assembly method, where both the surface mounted and through-hole components are joined commonly with the same reflow soldering step. In the paper, detailed evaluation of machine learning based prediction methods was performed, including artificial neural networks (ANN), adaptive neuro fuzzy inference systems (AFNIS) and gradient boosted decision trees to optimize the process parameters of pin-in-paste. The methods were presented for predicting the hole-filling of solder paste during stencil printing and to enable an evaluation of the aforementioned methods for the specific problem. Experiments were performed to obtain the required input data (hole-filling against the process parameters) for the training of the prediction tools. A testboard with plated through-holes of different diameters (0.8, 1, 1.1, 1.4 mm) was applied. Type 4 lead-free solder paste (particle size 20–38 µm) was deposited with stencil printing into the through-holes with different printing speeds (between 20 mm/s and 70 mm/s). The extent of hole-filling was investigated with X-ray analysis. The optimal structure for the prediction method was determined for every approach by varying the size and configuration iteration-wise. Optimal number of hidden neurons was 34 for the full data set and 18 for the smaller – incomplete- test cases. The optimal structure for the ANFIS consists of 8 and 6 membership functions respectively. Triangular and Gaussian membership function types and 7 different training methods were assessed. The ANN, which was trained with Bayesien Regularization (prediction error <15%) was found to be the recommended one for the PIP technology. A sensitivity analysis was carried out serving as a basis for managerial implications, which were also described in the paper. The obtained results aid in improving the quality and reliability of products assembled with pin-in-paste technology, also enable the more precise control in the wake of Industry 4.0 recommendations.