In today’s field of information dissemination, the widespread dissemination of fake news poses a serious challenge to the integrity of knowledge consumption. Harnessing advanced natural language processing (NLP) techniques, notably the BERT (Encoder Representation Transformers) model, has emerged as a promising approach to combat this pervasive issue. However, the training of such models, involving huge schedules and complex models, requires research into a methodology that can enhance the learning process. Recent studies have highlighted the potential of parallelization techniques, including data and model parallelization, to develop transformer-based models such as BERT. This paper focuses on the implementation of the parallelization technique to accelerate the training process, using the most accurate BERT learning model. The huge number of operations involved in BERT, in which billions of words are processed in each exercise cycle, is an immediate imperative to implement parallelization strategies. Aiming at a pragmatic application of the parallelization method, our study aims to develop a machine learning model to detect fake news. Leveraging the PyTorch network and a binary prediction classification framework, we aim to develop a robust model that can accurately discriminate between fake and genuine news articles. Through a comprehensive evaluation of the performance gain achieved by parallelization versus a serialized approach, our research attempts to contribute to the development of more effective and efficient mechanisms to detect fake messages within the limits of natural language processing.