3b238e271966a84a3dc-55a4 20250425075720803 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 04 30 2025 14 5 Student, Department of Information Technology, University of Mumbai, Mumbai (Maharashtra), India. Aqsa Shaikh Mariya Shaikh Student, Department of Information Technology, University of Mumbai, Mumbai (Maharashtra), India. Srivaramangai R. https://orcid.org/0000-0003-2723-6067 Head of the Department of Information Technology, University of Mumbai, Mumbai (Maharashtra), India. With the rapid uptake of mobile communications, cybercriminals have increasingly resorted to using SMS (Short Message Services) in the guise of phishing attacks commonly referred to as smishing (SMS phishing). Phishing SMS messages impersonate trusted organizations to persuade users into clicking malicious links, providing personal credentials, or installing malware. This paper reviews up-to-date advancements in machine learning for smishing detection, using insights derived from various studies on the subject. It looks into critical machine learning models such as Deep Learning models (CNN, LSTM), Logistic Regression, Random Forest, Support Vector Machines (SVM), and Gradient Boosting,) to classify messages as spam, phishing, or legitimate. It examines feature extraction techniques such as TF-IDF, N-grams, and natural language processing (NLP) in the hope of improving detection accuracy. In this way, it also looks at how cyber threat intelligence and real-world datasets such as SpamAssassin, the UCI Machine Learning Repository, and PhishTank can be used to build strong models. The results show that ensemble learning and hybrid deep learning techniques are better at finding things than traditional methods, and they do this without increasing the number of false positives. Challenges such as adversarial SMS attacks, multilingual phishing messages, and real-time detection limitations remain plausible. Future works need to look into adaptability to real-time models, CTI-based threat analysis, and understandable AI (XAI) detection transparency. Applying machine learning-driven smishing detection brings up the overall solution's intelligent automated approach and adaptive defense mechanisms against mobile phone phishing threats evolving, resulting in increased security for mobiles and, hence, their users. 04 30 2025 04 30 2025 28 33 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been sponsored or funded by any organization or agency. The independence of this research is a crucial factor in affirming its impartiality, as it has been conducted without any external sway. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijitee.D1068.14050425 https://www.ijitee.org/portfolio-item/D106814040325/