Access to information has become commonplace, thanks to digitalization, but that has also brought with it the rapid proliferation of disinformation, which has been detrimental to society as a whole. In this study, we focus on how it is possible to detect fake news using a hybrid Deep Learning (DL) model named Bi-directional Long Short-Term Memory Convolutional Neural Networks (BiLSTM-CNN). Considering the vast amount of both real and fake news, our proposed system can easily distinguish credible from non-credible information. The model integrates suitable data processing techniques and an appropriately developed structure that facilitates the processes, leading to practical outcomes with accuracy and enhanced processing time. Our findings show that the BiLSTM-CNN model is better placed than most other models in information detection, particularly disinformation detection, and is able to improve accuracy with reduced training periods. This paper discusses how the application of advanced DL helps today’s evolving disinformation challenges and undermines information ecosystem approaches such as BiLSTM-CNN. The paper is devoted to the development of bi-directional long short-term memory convolutional neural networks for fighting against disinformation in the modern digital world.