As the world becomes more data-driven, the challenge of detecting anomalies in real time has become more complex. Traditional methods, while effective in the past, no longer hold up in such volumes and variety with unprecedented levels of unpredictability. In an effort to overcome such limitations, scientists have recently relied on biologically inspired solutions such as Artificial Immune System (AIS)-based approaches, more specifically, Negative Selection Algorithms (NSAs). In this paper, three variations of NSA, including Random Negative Selection Algorithm (RNSA), Variable Detector Radius NSA (Vdetector), and Grid-based NSA (GNSA), are evaluated and tested over various datasets, including medical records, cybersecurity threats, and noisy real-world data. These algorithms have unique benefits: RNSA enhances the detection rate, Vdetector changes its behavior according to data, and GNSA balances speed with quality. Experimental results reveal that NSA-based approaches outperform the conventional approach on benchmark datasets under the standard parameters, including accuracy. These results also outline why it is necessary to embrace the new evolving systems over the conventional systems. These methods have been shown to detect anomalies far more accurately and adapt in real time. NSAs make more reliable, intelligent security and monitoring systems a possibility.