In this research, the prevailing issue we address is the over-generalized perspective of spam/ham (non-spam) classification. Despite the intricacies of spam classification, reliance on user feedback may inadvertently skew filters to misclassify legitimate and malicious emails, as users are prone to flag innocuous commercial mail as spam rather than unsubscribing. Current spam datasets have a propensity to include such user-flagged spam which can lead to further misclassification, leading to filters biased against warranted commercial correspondence. Motivated to address this concern, we introduce two new classification categories that delve deeper into the nuances of spam. 'Warranted spam', refers to consensual communications, from a credible source with transparent and safe opt-out mechanisms, and 'unwarranted spam' describes unsolicited messages, often of a malicious nature. Utilizing these classifications, we propose an innovative and dynamic 'warranted spam' dataset that seeks to pave the way for researchers to develop more sophisticated spam filtering techniques. Furthermore, our study delves into pioneering machine learning and natural language processing approaches, harnessing our dataset's potential. The overarching aspiration of our work is to augment online safety, preserve brand integrity, and optimize both the user experience and the efficacy of email marketing campaigns.