###### Abstract  
This study examines multi-lexical data sources, utilizing an extracted dataset from an open-source corpus and the Global Terrorism Datasets (GTDs), to predict lexical patterns that are directly linked to terrorism. This is essential as specific patterns within a textual context can facilitate the identification of terrorism-related content. The research methodology focuses on generating a corpus from various published works and extracting texts pertinent to \"terrorism\". Afterwards, we extract additional lexical contexts of GTDs that directly relate to terrorism. The integration of multi-lexical data sources generates lexical patterns linked to terrorism. Machine learning models were used to train the dataset. We conducted two primary experiments and analyzed the results. The analysis of data obtained from open sources reveals that while the Extra Trees model achieved the highest accuracy at 94.31%, the XGBoost model demonstrated superior overall performance with a higher recall (81.32%) and F1-Score (83.06%) after tuning, indicating a better balance between sensitivity and precision. Similarly, on the GTD dataset, XGBoost consistently outperformed other models in recall and the F1-score, making it a more suitable candidate for tasks where minimizing false negatives is critical. This implies that we can establish a specific co-occurrence and context within the terrorism dataset from multiple lexical data sources in effectively identifying certain multi-lexical patterns such as \"Suicide Attack/Casualty\", \"Civilians/Victims\", and \"Hostage Taking/Abduction\" across various applications or contexts. This will facilitate the development of a framework for understanding the lexical patterns associated with terrorism. c 202517820 been addressed. While existing work endeavors to resolve pronouns, word hypernymy, or phrasal verbs, some even attempted to address the full extent of fine-grained lexical correspondences necessary for a truly predictive model [3]. Text classification is one of the important tasks in Natural Language Processing (NLP), which deals with a variety of genres such as categorizing news articles, deciding spam or non-spam emails, identifying the sentiment orientation of product reviews, determining the genre of a movie based on its plot summary, and predicting the developmental level or literary style of free-written children's essays [1; 2]. For predictive modeling with target labels such as news topics, spam, sentiment, and movie genres, the features used in the study are the word unigrams and bigrams between lexical items in a sentence or document [4]. Additionally, bag-of-words are used, and the document is represented through histogram-like statistics of lexical items--in other words, frequency [5]. The target labels are generated or assessed by human coders, and inter-coder evaluations are applied to the target labels based on the evaluations of human coders who judge each target label [1]. The paper's core objective is to leverage textual data analysis to identify and predict terrorism-related lexical patterns. This has a direct connection to the internet, as much of the data analyzed (e.g., social media posts, news articles, blogs) are sourced from online platforms. The internet serves as a vast repository of unstructured textual data, which are increasingly being used by terrorist organizations for communication, recruitment, and propaganda. By analyzing these data, the framework proposed in the paper can help in the early detection of potential threats, thereby enhancing cybersecurity intelligence. The internet is the primary medium through which terrorist organizations disseminate information, communicate, and recruit members. The paper's framework can be applied to monitor and analyze online content in real-time, providing a proactive approach to counterterrorism.