The fluctuating and changing cybersecurity threats call for effective, intelligent systems that can, on the one hand, recognize anomalies in the device usage. Anomaly Detection has come to the fore of cyber security by recognizing unintended behavior that might be a result of malicious activity or security breaches. This research is distinguished by Auto encoders, Recurrent Neural Networks (RNNs), Variational Auto encoders (VAEs), and Isolation Forest for anomaly detection from device usage data. By measuring precision, recall, and F1score, along with training time, these models were compared to each other with the help of a dataset that covers devices key usage parameters. The outcome shows that the variational auto encoders (VAEs) achieve the accuracy and precision levels that are necessary for the interpretation of complex, high dimensional datasets, and thus are an excellent choice for nuanced anomaly detection tasks. RNNs are very good at identifying sequential dependency in time series data, however, they need more computational power to run. Autoencoders provide a middle ground of efficiency and accuracy, which makes them applicable for systems that need moderate detection capability but lower computational overhead. Nevertheless, Isolation Forest is the most effective in terms of computation and consequently, enables the generation of quick and interpretable results, which are indeed ideal for resource-constrained environments. The way it demonstrates that each of these algorithms has a set of trade-offs, and the importance of choosing the proper model according to the specific use case.