Ransomware attacks are emerging as a critical cybersecurity threat, affecting not only individuals but also businesses and critical infrastructure. This study does a thorough literature analysis to look into ransomware attacks and explore detection techniques. The research includes a review of six pieces of research that provide a profound understanding of the historical development and expansion of ransomware, its significant financial consequences, and the numerous challenges confronting both people and companies in their efforts to protect against these threats. The research seems to focus on machine learning-based detection, deep learning approaches, and the critical importance of explainable AI to comprehend the intricacies of the detection process. The research highlights the need for adaptable detection systems to navigate the dynamic threat landscape of ransomware, highlighting the limitations of machine learning methods like concept drift. The comprehensive review of the literature offers useful insights on attacks with ransomware and detection techniques. It emphasizes the significance of dynamic detection systems, explains the complexities of machine learning models, and emphasizes the potential benefits of using deep learning techniques. Subsequently, this paper enhances a theoretical and analytical comprehension of ransomware detection, providing the foundation for future research efforts targeted at developing effective countermeasures for this emerging cybersecurity threat.