Email is one of the most essential and useful communication channels of this advanced world. The continuous growth of email user has led to a massive rise of unsolicited emails also known as spam emails. Hence, appropriately managing and classifying this huge number of emails pose a critical challenge. Despite frequent attempt in creating more reliable and effective solution, there is room for improvement. In this paper, an efficient spam email detection technique is proposed and examined on a combined training dataset after applying feature engineering techniques. Thereafter the proposed model is tested using combination of five different datasets and several machine learning algorithms including SVM, SVC, Naïve Bayes, KNN, logistic regression, decision tree, etc. Experimental result shows high detection rate while reducing training time. Comparative analysis highlights the remarkable outcome of this experiment as compared to existing literature. The overall accuracy achieved by the suggested approach is 99.39%.