Precise force-torque sensing plays a fundamental role in humanoid robot control, impacting tasks from manipulation to movement. A conventional technique for cali- brating 6D force-torque sensors linear regression, which in some cases could not give good results, especially in the field of robotics. The goal of this thesis is to im- prove the accuracy and efficiency of 6D force-torque sensor calibration in humanoid robotics by introducing a novel neural network-based method. We employ machine learning methods, particularly neural networks, to simulate the cross-axis coupling effects and nonlinearities present in the sensors. As part of the process, data are collected during in-situ experiments, meaning that the robot has the sensor installed already.