Email Spamming has been recognized as one of the most dangerous cyber-attacks these days. As email is more encouraging platform for the communication mechanism these days, it is accessible to everyone across the world with the help of internet. Hence it has to be protected in order to reduce the cyber-attacks which involve loss of organizational property. The previous spam-filtering technologies include humanly detection of certain keywords and blocking the spam-sending domains which are recognizable. Spamming of emails is on the rise as the number of internet users grows, resulting in the leakage of personal information from users. Thus, detecting these email spams is critical in order to reduce illegal and unethical behavior, as well as phishing and fraud. As a result, ongoing research into email spam detection has been conducted utilizing a variety of machine learning algorithms with varying levels of accuracy. Using the required techniques of machine learning in this project, the regular words which are used in spam emails are easily identified using the pre-occupied data set called stop-words. This proposed system tries to recognize a recurrent word group which are used mostly that are classed as spam using machine learning techniques. The machine learning model that has been using is an early-trained model with feedback that can tell the difference between a correct and an ambiguous output.