OBJECTIVE: Intracranial pressure (ICP) monitoring is widely used in the management of patients with traumatic brain injury (TBI). The morphology of the ICP waveform is considered to provide valuable insights into cerebrospinal compliance. This paper proposes a topological data analysis (TDA)-based methodology for ICP morphological analysis. METHODS: About 1.2 million ICP waveforms from 60 TBI patients are utilized to construct a data map. This map is used for near real-time ICP morphology classification, subpeak identification, and Big Data visualization. The method allows ICP morphology class labels and subpeak labels annotated by SMEs on a subset of representative waveforms to quickly propagate to millions of unlabeled waveforms, which significantly reduces labelling effort. RESULTS: The proposed visualization allows the overlay of various ICP morphological features (e.g., P2/P1 ratio, ICP peak pressure) to provide insights into patients' physiological condition. The method is validated on 10,000 ICP waveforms from 10 patients, achieving an overall waveform classification accuracy of 96.1% and subpeak identification accuracy of 97.3% . CONCLUSION: The proposed method can track subtle changes in ICP waveform morphology, offering insight into evolving intracranial compliance beyond mean ICP values. By enabling real-time, interpretable monitoring, the method provides a tool to support individualized management and early intervention in TBI patient care.