State estimation is a fundamental task in the operation of smart grids, guaranteeing that the system can consistently be monitored and forecast grid conditions for effective administration. Although conventional techniques for state estimation are efficient, they encounter difficulties in adjusting to progressively intricate grid contexts. The goals of this Thesis include assessing whether AutoML tools can outperform traditional methods, paving the way for future development of automated state estimation solutions in smart grids. In order to assess the efficacy of AutoML models in this task, synthetic data was produced using the pandapower tool on two grids of varying sizes to replicate diverse operating scenarios.