With the increasing reliance on mobile communication, detecting spam messages sent via Short Messaging Service (SMS) has become more important. This advent has created a new era for spam in people’s lives, one that calls for quick attention and automatization in categorizing messages. This study analyzes three machine learning algorithms—Logistic Regression, Naive Bayes, and Decision Tree resulting in the binary classification of SMS messages into either spam or not spam (ham). To achieve effective spam detection, the study highlights the significance of feature engineering, model selection, and evaluation metrics such as accuracy, precision, recall, and F1-score. The research challenges, including unbalanced data, changing spam strategies, and the requirement for scalable solutions, are handled in this study. During experimentation, it was observed that Logistic Regression increased performance by 98.07% accuracy. The results also showed the advantages and disadvantages of each model, providing guidance on which strategy, is best for SMS spam filtering apps in the real world. This analysis aims to give readers a thorough grasp of existing approaches and how they might be used to improve the effectiveness and security of mobile communication systems.