Unwanted messages sent in large quantities, typically endorsing goods or frauds, are known as spam emails. These emails clog inboxes, irritating users and making it more difficult to locate genuine information. Spam emails are annoying, but they can also reduce internet speed by using bandwidth because they need a lot of data to filter and download. Because many spam emails contain malware or phishing attempts meant to steal personal information like names and addresses, they constitute a severe security concern. These may result in sensitive data being accessed without authorization or identity theft. The pervasive problem has prompted researchers to create models for more accurate spam detection. A model that was suggested to detect spam emails was 98% accurate. This model most likely employs machine learning methods to discern between spam and legitimate emails by looking for trends in the email’s metadata and text. The methodology improves cyber security by avoiding threats from reaching users and helps reduce the manual labor necessary to filter emails by automating spam identification.