The escalating prevalence of ransomware attacks necessitates the development of innovative detection methodologies. Traditional detection techniques, such as signature-based and behavioral analysis methods, often fall short in identifying novel or obfuscated ransomware variants. To address these challenges, the concept of Semantic Flow Mapping has been introduced, offering a novel approach to ransomware detection. This methodology employs graph theory to model system processes and their interactions, representing processes as nodes and communication channels or resource accesses as edges. By applying statistical measures, the system assesses normalcy within these graphs, identifying anomalies through deviations from established baselines. Machine learning techniques, including clustering algorithms and neural networks, are utilized to model the complex, non-linear relationships characteristic of system behaviors, thereby enhancing the detection of ransomware activities. The mathematical framework is designed to be computationally efficient, ensuring real-time applicability without imposing significant performance overheads on the system. Empirical evaluations demonstrate the method's proficiency in maintaining low false positive and false negative rates, showing its reliability in distinguishing between legitimate and malicious processes. Furthermore, scalability assessments reveal that the methodology can be effectively deployed across networks of varying sizes, maintaining consistent performance without imposing substantial computational overhead.