Anomaly detection is an important task in many areas, including cybersecurity, healthcare, and finance, where it is crucial to identify abnormal behaviors or patterns. However, traditional anomaly detection methods can be sensitive to outliers and lack robustness to distributional changes in the data. In order to overcome these limitations, a hybrid and ensemble multi-layered approach for robust anomaly detection has been proposed in this work. The approach consists of a combination of multiple one class classifiers, each trained on a different subset of the data, and a Variational Autoencoder (VAE). The one class classifiers are used to identify local anomalies, while the VAE is used to model the underlying distribution of the data and detect global anomalies. These are the two sets of hybrid features. Next, different one-class classifiers have their strength and limitations. The final decision on whether an instance is anomalous is made by combining the outputs of the one class classifiers and the VAE through an ensemble learning mechanism. Thus, we propose an adaptive weightage approach that gives the weight to each classifier. Next, these reduced hybrid features are passed as input to the second phase. In this phase, we have a deep neural network that learns the patterns of the dataset and generates an adaptive dynamic threshold to discriminate the input feature as an anomaly or benign. The results showed that the hybrid and ensemble multi-layered approach outperforms state-of-the-art anomaly detection methods in terms of robustness and accuracy. Furthermore, the combination of the one class classifiers and the VAE provides a complementary approach that captures both local and global anomalies, making the approach more comprehensive than traditional methods. In conclusion, this work presents a novel hybrid and ensemble multi-layered approach for robust anomaly detection that can effectively address the limitations of traditional methods. The approach has the potential to be applied in a wide range of applications.