Transfer learning has been extensively used to build bearings remaining useful life prediction models under multi-operating conditions. However, most models only consider single-target domain, the generalization of the model under across equipment-operating conditions cannot reach the development of modern industry. Therefore, this study proposes a multisource-multitarget domain adaption transfer learning network (MSMTDATLN) hoping to solve across equipment-operating conditions prediction. Constructing a one-total and multi-branch network in MSMTDATLN to extract domain-invariant and domain-specific features from multisource-multitarget domains, adaptively eliminating the differences between domain-specific features, enhancing adaptive regression capabilities by aligning output decision boundaries. Improving model generalization in multitarget domains by the above methods, while enabling end-to-end training and inference. Experimental results on across equipment-operating conditions datasets validate the proposed method reduces RMSE index by 7% compared with other advanced algorithms while providing better model generalization, which can provide theoretical foundation and technical support for the bearing prediction tasks across equipment-operating conditions.