Applied AI and Machine Learning Journal (AIML) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-quality original research papers, review articles, and case studies in the fields of artificial intelligence (AI) and machine learning (ML). The journal aims to advance theoretical foundations, innovative methodologies, and real-world applications of intelligent systems that contribute to technological and scientific progress. AIML serves as an interdisciplinary academic platform for academics, researchers, and practitioners to exchange ideas, foster collaboration, and disseminate cutting-edge research findings. The journal covers a broad range of topics, including deep learning, natural language processing, computer vision, robotics, data analytics, and intelligent decision support systems, reflecting the rapidly evolving landscape of AI and ML research. By encouraging global scholarly contributions, Applied AI and Machine Learning Journal (AIML) seeks to promote the ethical, responsible, and sustainable development of artificial intelligence and machine learning technologies. The journal aims to bridge theory and practice by supporting research that delivers meaningful technological innovation and positive societal impact at local, national, and global levels.