In this letter, we present the hierarchical recurrent-inception residual transformer (HRIRT), an innovative deep neural network architecture designed for accurate hand force estimation in human–robot collaboration (HRC). The HRIRT combines recurrent layers, inception modules, residual connections, transformers, and Time2Vec feature engineering within a hierarchical framework to adeptly capture the complex spatiotemporal dynamics of hand force data. Our evaluation spans three dimensions of HRC—1-D, 2-D, and 3-D hand force estimation—leveraging data from force myography (FMG) sensors to train and test the model's performance. The HRIRT demonstrates exceptional accuracy and robustness across varied interaction scenarios with the Kuka Robot. The 1-D interactions focus on linear force applications, while 2-D and 3-D interactions involve more complex spatial movements, showcasing the model's capability to generalize across different force interaction contexts. In 1-D scenarios, HRIRT achieved a 93.76% R-Square (R2) score, significantly outperforming transfer learning with cross-domain generalization and stacked convolutional neural network (CNN) models. In addition, in 2-D and 3-D force estimations with R2 scores of 94.25% and 91.61%, respectively, the HRIRT showcased exceptional accuracy and maintained low error rates across root-mean-square error, normalized mean square error, and mean absolute error metrics. These results highlight HRIRT's potential as a powerful tool for real-time precise hand force estimation in diverse HRC applications.