The rapid growth of digital communication platforms has significantly increased the volume of SMS and email messages exchanged daily. While these technologies enhance connectivity and information sharing, they have also become primary channels for spam, phishing, and fraudulent activities. Spam messages not only cause inconvenience but also pose serious security and privacy risks to individuals and organizations. Therefore, developing an accurate and efficient automated spam detection system has become an essential requirement. This study proposes a robust machine learning framework for intelligent classification of spam and legitimate (ham) SMS and email messages using advanced text analytics techniques. The system incorporates comprehensive preprocessing methods, including text cleaning, tokenization, stop-word removal, and normalization, followed by feature extraction using techniques such as TF-IDF and word embeddings. Multiple machine learning algorithms, including Naïve Bayes, Support Vector Machines, Logistic Regression, Random Forest, and Gradient Boosting, are implemented and comparatively evaluated. To further enhance predictive performance, ensemble learning strategies are employed to combine the strengths of individual classifiers. Experimental results demonstrate that the proposed hybrid framework achieves high accuracy, precision, recall, and F1-score across benchmark datasets. The system effectively minimizes false positives and false negatives, thereby improving reliability in real-world applications. The proposed approach contributes to the development of scalable, intelligent, and adaptive spam filtering systems capable of handling evolving spam patterns in modern communication networks.