Ransomware remains a critical cybersecurity threat, with attacks surging in recent years, including over 4,399 incidents in 2023, nearly doubling from 2022, and total payouts exceeding $1 billion. This study examines the effectiveness of AI-based algorithms in ransomware detection, focusing on metrics such as accuracy, precision, recall, and false positive rates. Neural Networks achieved exceptional accuracy of 99.9%, demonstrating adaptability to evolving ransomware patterns, while hybrid approaches like SVM combined with Adaboost reached 99.54% accuracy with minimal false positives. Time-efficient models, such as Recurrent Neural Networks (RNN), showcased real-time detection capabilities, balancing speed and accuracy. Hybrid algorithms further enhanced robustness by integrating multiple models to analyze static and dynamic data. These findings highlight the critical role of AI-driven techniques, particularly Neural Networks and hybrid approaches, in building reliable and adaptive defense systems against evolving ransomware threats.