Escalating threats from increasingly sophisticated ransomware attacks pose significant risks to data integrity, financial stability, and operational continuity for organizations across sectors. Addressing these threats requires a novel detection framework that overcomes the limitations of traditional signature-based and machine learning-based approaches, particularly their vulnerability to adaptive ransomware tactics and resource constraints. The proposed Dynamic Threat Imprint Analysis (DTIA) method leverages real-time behavioral analysis combined with adaptive learning mechanisms, enabling it to detect both known and previously unseen ransomware variants with high accuracy. Experimental evaluation demonstrates that DTIA achieves a 98.7% detection rate for known ransomware while effectively adapting to new variants, maintaining a 95% detection rate for previously unseen threats. Additionally, DTIA operates with minimal impact on system performance, showcasing scalable and efficient detection capabilities suited for deployment in environments with diverse computational resources. These findings demonstrate the potential of DTIA as a sophisticated and adaptable solution for the evolving challenge of ransomware detection, enhancing cybersecurity resilience through advanced behavioral analysis and dynamic learning capabilities.