This paper addresses how sensor networks and machine learning create disruptive synergies in intruder detection due to targeted improved accuracy, minimized false positives, and real-time optimization. A literature review is offered on how the methodologies of intruder detection systems changed, with emphasis on the prime symbiosis that machine learning algorithms share with sensor networks. Methodologically, various sets of datasets has been utilized in training with strong preprocessing techniques utilized for enhancing data. The discriminative features that are useful in intrusion detection restrict the feature selection with emphasis on such features. The central task, here, is machine learning models formulated and implemented in the sensor network scenario. Hence, the mechanism of anomaly detection, adopted in the intrusion detection algorithm, provides intruder identification in real time with negligible latency in both time and accuracy. The test platform is relentlessly pounded into all the situations applicable to actual authentication parameters of the system, e.g., precision, false alarms, and elapsed time, under heavy scrutiny. The effects encompass tremendous enhancement in detection efficiency, grossly minimized false alarms, and excellent improvements in real-time response. An Innovative one from the intruder detection system viewpoint enters the educational world when talking about an implementable solution which highly likely can revolutionize physical security. It is flexible to different situations and even finds itself capable of being put inside already available structures, and thus it brings the prospect of being an immensely revolutionary point in this arena that would continue further with machine learning and sensor network incorporation for smart provisions of security.