Ransomware has emerged as a critical and rapidly evolving cybersecurity threat, significantly impacting sectors such as healthcare, finance, and government infrastructures. This paper presents a comprehensive survey of contemporary ransomware detection techniques, focusing on machine learning (ML) and deep learning (DL) methodologies, which have shown promise in adapting to the rapidly changing landscape of ransomware attacks. The survey includes a detailed comparative analysis of static, dynamic, and hybrid detection models, highlighting their respective advantages and limitations.