Traditional ransomware detection methods often fail to keep up with the rapidly evolving cyber threats, especially in critical sectors like finance and healthcare. With 59% of organisations affected by ransomware in 2023, the need for effective solutions is undeniable. This research addresses significant gaps by developing adaptive and explainable machine-learning models that can match the fast-paced evolution of ransomware. It also tackles the issues of lacking standard datasets, inadequate feature analysis, and the growing threat of adversarial machine learning attacks. These contributions are poised to significantly enhance the resilience of organisations against ransomware, making them better equipped to protect their most vital assets.