The environment of danger has increased significantly as a consequence of the increasing reliance on mobile applications for banking, healthcare, communication, and commercial operations. Malware, phishing, and zero day exploits are instances of complex and dynamic cyberattacks that are difficult for traditional security systems to recognize. Advanced capabilities for proactive threat identification and prevention are provided by artificial intelligence AI , particularly machine learning ML and deep learning DL . Artificial intelligence AI powered techniques analyze system performance, network traffic, permissions, and application behavior to identify negative patterns immediately. The application of AI for enhancing mobile app security is addressed in this investigation along with its primary methods, benefits, challenges, and potential future study topics. The rapid expansion of mobile applications in sectors as organizations, e commerce, healthcare, education, and finance drove both people and businesses far more a target for sophisticated cyberthreats. Malware, ransomware, spyware, phishing crimes data breaches, and zero day exploits have all made mobile platforms—especially Android and iOS— the most prevalent targets. Conventional safety precautions, who mostly rely on rule based systems and signature detection, are becoming less and less effective in identifying emerging and multifaceted threats. More intelligent and fluid security solutions need to be developed because these conventional approaches have high false positive rates and are unable discern not planned assaults. Artificial intelligence AI , particularly machine learning ML and deep learning DL , has grown into an important tool in terms of threat prevention and mobile application security.AI driven process can automatically critique large scale mobile app datasets and uncover hidden patterns and odd behavior that could indicate malicious intent. ML models can use supervised, unsupervised, and semi supervised learning techniques in order to assess if a program is malicious or benign based on data such as permissions, API calls, opcode sequences, network traffic patterns, and system call behaviors. Significantly developing detection skills happen to be deep learning architectures such Convolutional Neural Networks CNNs , Recurrent Neural Networks RNNs , and Graph Neural Networks GNNs , which automatically extract intricate and high dimensional data. Especially professional at detecting zero day attacks, hidden malware, and advanced persistent threats are these models. Mobile security solutions driven the AI generally utilize static, dynamic, and hybrid analytic methods. In a bid to find vulnerabilities and odd patterns, and static analysis looks at application code and metadata without processing them. Dynamic analysis monitors runtime behavior, including memory usage, network communication, and user interactions, enabling real time anomaly detection. For more detection accuracy and decrease false positives, hybrid approaches mix both of the approaches. The security of the mobile ecosystem continues to be strengthened by AI powered intrusion detection systems IDS , fraud detection models, and phishing ways to detect using Natural Language Processing NLP .Given its numerous perks, AI driven mobile security has drawbacks, including issues with model interpretability, computational cost on smartphones with limited resources, adversarial attacks against AI models, and data privacy concerns. Explainable AI XAI for transparent decision making, edge based AI systems for real time on device protection, and federated learning for privacy preserving model training are the main areas of emerging study. All things considered, AI driven attacks offer a viable and developing paradigm for intelligent, scalable, and proactive mobile app security and threat avoidance. Additionally, the scalability and reactivity of mobile security systems are improved by the combination of cloud intelligence with edge based AI. Security models may be updated with new threat intelligence on a regular basis while protecting user privacy by utilizing distributed learning processes. By incorporating real time automated reaction systems, harmful activity can be immediately contained, minimizing possible damage and data breaches. In dynamic mobile contexts, this self learning and adaptive security architecture guarantees ongoing defense against changing cyberthreats. Machine Learning ML , Deep Learning DL , Mobile Application Security, Threat Detection, Threat Prevention, Malware Detection, Android Security, iOS Security, Intrusion Detection Systems IDS , Behavioral Analysis, Static Analysis, Dynamic Analysis, Hybrid Security Models, Zero Day Attack Detection, Phishing Detection, Adversarial Machine Learning, Federated Learning, Edge AI Security, Cybersecurity, Zero Day Exploits ,Polymorphic Malware, Behavioral Biometrics 54-62 Advances in Computer Applications and Information Technology Special Issue Rutuja Kumare | Mohini Belsare