The escalating complexity of cyber threats requires increasingly sophisticated approaches to automated detection, particularly within the domain of ransomware. Existing methods, such as signature-based and heuristic detection, often fail to recognize novel ransomware variants due to their reliance on predefined, static indicators or anomaly-based heuristics, leading to high false positive and false negative rates. DeepCodeLock addresses these limitations through a deep learning framework that leverages convolutional and recurrent neural network layers to analyze ransomware behavioral patterns comprehensively, allowing for the extraction of high-level features indicative of malicious intent. By processing system activities like file access, network connections, and process hierarchy structures, the model autonomously generates a dynamic, composite behavioral signature for each monitored activity, enhancing detection precision. Experimental results demonstrated that DeepCodeLock achieved significant improvements in detection accuracy, reduced false positive and negative rates, and maintained low latency and resource utilization across varied network sizes, establishing its practicality for real-time, large-scale cybersecurity applications. Overall, DeepCodeLock's methodological innovations emphasize its utility as an adaptive and scalable ransomware detection system, offering new possibilities in protecting critical infrastructures from emergent cyber threats.