Spam detection plays a vital role in making digital communication secure and work effectively in the digital era. Traditional methods to block spam emails and messages such as rule-based filters and blacklists show their limits because spammers keep finding new ways to bypass filtering systems. This research analyzes machine learning methods for spam detection while showing how they address the weaknesses of traditional detection methods. This study shows the effectiveness of these text analytics strategies through evaluation of Naïve Bayes, Support Vector Machines (SVM), Random Forest, Gradient Boosting, and advanced deep learning Long Short-Term Memory (LSTM) networks. Deep learning techniques show better results than other systems by finding spam emails with both precision and dependability. While major progress has been made, ongoing challenges including imbalanced datasets, adversarial attacks, and the evolving nature of spam tactics still persist. This research concludes by recommending various future directions to improve spam detection such as building multilingual models plus developing robust adversarial defenses accompanied by explainable AI (XAI) solutions to enhance the adaptability and trustworthiness of spam detection technologies.