Enhancers are short DNA cis-elements that can be bound by proteins(activators)to increase the possibility that transcription of a particular gene will occur.The Enhancers per-form a significant role in the formation of proteins and regulat-ing the gene transcription process.Human diseases such as can-cer,inflammatory bowel disease,Parkinson\'s,addiction,and schizophrenia are due to genetic variation in enhancers.In the current study,we have made an effort by building,a more robust and novel computational a bi-layered model.The representative feature vector was constructed over a linear combination of six features.The optimum Hybrid feature vector was obtained via the Novel Cascade Multi-Level Subset Feature selection(CM-SFS)algorithm.The first layer predicts the enhancer,and the secondary layer carries the prediction of their subtypes.The baseline model obtained 87.88％of accuracy,95.29％of sen-sitivity,80.47％of specificity,0.766 of MCC,and 0.9603 of a roc value on Layer-1.Similarly,the model obtained 68.24％,65.54％,70.95％,0.3654,and 0.7568 as an Accuracy,sensitiv-ity,specificity,MCC,and ROC values on layer-2 respectively.Over an independent dataset on layer-1,the piEnPred secured 80.4％accuracy,82.5％of sensitivity,78.4％of specificity,and 0.6099 as MCC,respectively.Subsequently,the proposed pre-dictor obtained 72.5％of accuracy,70.0％of sensitivity,75％of specificity,and 0.4506 of MCC on layer-2,respectively.The proposed method remarkably performed in contrast to other state-of-the-art predictors.For the convenience of most exper-imental scientists,a user-friendly and publicly freely accessi-ble web server@/bienhancer dot pythonany where dot com/has been developed.