Force/torque sensors are essential tools that enable robots to effectively interact with their environments. Existing calibration methods often fail to capture inter-axis nonlinearities and coupling effects, particularly when available calibration data are sparse and discrete. To address this challenge, the presented approach employs a Deep Neural Network (DNN) that learns both the scaling and the direction of the input-output relationship. The method works by extracting the absolute magnitude and unit vector from the raw N-dimensional sensor output values, which can vary among sensors. The DNN takes this N-dimensional input and produces a 7-dimensional output—comprising a corrected 6D unit vector representing the desired force-torque direction and a scaling factor. The final measurement is then constructed by combining the output unit vector, the learned scaling factor, and the original input magnitude. This approach simplifies the calibration problem to a linear mapping along one axis, enabling the model to generalize well under limited training conditions while leveraging the DNN’s strength in capturing nonlinear inter-axis relationships. The proposed DNN was trained and evaluated on both artificially generated and real-world datasets, and its performance was compared to two baseline models: a commonly used linear transformation model and a comparative DNN approach from the literature. On generated data, the proposed DNN achieved an RMSE of 36.9 ± 3.44, outperforming the comparative DNN (48.3 ± 4.47) and the linear transformation model (62.3±0.76). Similar improvements were observed on the real-world dataset. Although these results are promising, they are based on artificially generated data and a single real-world dataset from one specific sensor. Further validation and more extensive testing are necessary. Nonetheless, the gains indicated here suggest meaningful potential for improved calibration strategies in force-controlled robotic applications, even under limited training conditions.