In the era where companies need to work remotely, the relevant usage of advanced technology comes with a side threat: the threat of ransomware attacks. To formulate effective responses to ransomware attacks, we will explore whether advanced deep learning algorithms can be an option. The paper examines how a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architecture can tackle the malicious usage of modern cyber technology, specifically in identifying and tackling ransomware bonuses. For the hybrid LSTM–GRU model, accuracy measures of 97% with performance measures given as precision, recall, and F1-score standing at 0.99, 0.97, and 0.98, respectively. For cases of ransomware, the respective scores were precision, recall, and F1—0.96, 0.98, and 0.97. The explanation, using confusion matrices and ROC curves, depicts the model’s strength in minimizing false positives and negatives, allowing it to be suitable for its intended use. These results endorse the competencies of hybrid recurrent networks in understanding the nuances of different time frames, which can significantly assist in fighting against the changing scenario of ransomware. This research contributes to forming new types and approaches to cybersecurity measures, increasing protection efficiency against hacker attacks.