Intelligent neuromorphic computing has been increasingly used in recent years to improve machine learning approaches for automatically detecting anomalies. This review paper provides a thorough analysis of the progress achieved in this interdisciplinary field in recent years, namely in the areas of machine learning, computer vision, and pattern recognition. The analysis is based on the findings published in high-ranking publications. The combination of neuromorphic computing concepts and machine learning algorithms has enabled the development of innovative methods for detecting anomalies. These methods are known for their effectiveness, flexibility, and capacity to handle data in real-time or almost real-time situations. The main topics examined include the application of spiking neural networks, event-based processing, and bio-inspired computing models to address the difficulties presented by intricate data patterns and changing abnormalities. In addition, this review consolidates the advancements made in computational neuroscience, adaptive systems, and edge computing paradigms to enhance the efficiency and practicality of these intelligent systems. This paper aims to offer a thorough overview of the current advancements in intelligent neuromorphic computing-assisted machine learning for autonomous anomaly detection. It achieves this by analysing recent literature, identifying emerging trends, and outlining potential areas for future research.