Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.