This thesis investigates the use of NVIDIA Isaac Sim, a robotics simulation platform built on NVIDIA Omniverse, designed to simulate and validate AI- driven robotic solutions within physically realistic virtual environments. The main objective is to assess the effectiveness of synthetic datasets generated through Isaac Sim and their applicability in industrial scenarios such as depalletization and bin-picking. Synthetic data—artificially produced information obtained from virtual simu- lations—represents a strategic asset when real data is costly, difficult to acquire, or prone to bias. To this end, a test environment faithfully reproducing real oper- ating conditions was developed in Isaac Sim, introducing variability in position, orientation, lighting, textures, materials, and in the physical characteristics of boxes/objects and containers.