Email is considered one of the significant mediums of communication and widely used in recent times. However, the evolving nature of different cyberthreats can temper effective communication and alter the potential message. Disrupting nature of spam is still a major challenge in the email security aspect which requires inventive methods to eliminate cutting-edge sophisticated spam emails. Machine learning is one of the significant dimensions in the literature and proves the effectiveness and robustness which distinguish spam emails from any mailbox. In this paper, a hybrid machine learning-based approach named the majority voting ensemble method has been proposed that combines multiple techniques and it can produce the best accuracy compared to some existing machine learning methodologies. The proposed system aims to enhance accuracy, increase precision value, and adapt to evolving spam tactics. The experimental analysis with the predefined dataset proves the effectiveness of the proposed mechanism.