Sarcasm is a nuanced form of linguistic irony in which spoken or written words convey a meaning contrarytotheirliteralsense, making automated detection a persistent challenge in natural language processing (NLP). Existing computational methodsstrugglebecause sarcasm relies heavily on contextual cues, prior knowledge, and delivery style. This paper presents Hybrid-Sarcasm, aHybridMachine Learning (HML) framework that integrates three distinct feature categories—lexical, sarcastic, and contextual—toclassifytweets as sarcastic or non-sarcastic. The proposed approach introduces a sarcasm-specific feature set combinedwithensembleclassification techniques. Experiments on a Twitter-based dataset demonstrate that the HML classifier achieves 95.30%accuracyonsarcastic feature sets, outperforming baseline methods including K-Nearest Neighbor, Random Forest, Support Vector Machine,andDecision Tree. These results confirm that sarcasm-oriented features substantially improve classifier performance across all models evaluated. Keywords—sarcasm detection, hybrid machine learning, natural language processing, sentiment analysis, Twitter, featureextraction,text classification