As the trend of miniaturization of electronic components has grown, demands for advanced microelectronics packaging development have also increased. At the same time, however, this trend raises concerns of unreliable assembly processes that are caused by defective packaging interconnections. In particular, the defects can be induced by non-coplanarity and unpredictable structural deformation of interconnections. When a slope of the die exceeds a certain degree, connectivity between components in the package may fail, which results in warpage or electrical power loss. To control this issue, thermo-compression bonding has been developed to globally apply heat and pressure into the die while the substrate is maintained at a low stage temperature. Therefore, in order to effectively handle these issues, strongly coupled thermal and structural analysis is inevitable. In this research, a simulation-based optimal design of thermo-compression bonding is developed to achieve better packaging reliability in the time transient domain. The proposed framework clearly demonstrates how the multivariate uncertain parameters can be generated. Also, it suggests how the multivariate uncertainty can be propagated through the classification approach, i.e., artificial neural network. The classification approach is then utilized to estimate the reliability of the system. The efficacy of the proposed framework is demonstrated with a practical example of an advanced packaging system which is utilized in actual commercial products. Ultimately, this study demonstrates how the strong coupling optimization method can be utilized in the actual packaging system.