Information For Readers

About The Journal

International Journal of Machine Learning (IJML) is a peer-reviewed international journal dedicated to publishing high-quality research in machine learning and artificial intelligence. The journal welcomes original research articles, review papers, and applied studies that demonstrate novelty, methodological rigor, and practical relevance.

IJML covers a broad range of topics, including supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision, explainable AI, trustworthy AI, and interdisciplinary real-world applications. The journal is committed to maintaining high editorial standards, ethical publication practices, and transparent peer-review processes to support global scholarly communication.

Aims & Scope

The International Journal of Machine Learning (IJML) publishes high-quality scholarly works in the theory, methods, and applications of machine learning and artificial intelligence.

The journal welcomes submissions in (but not limited to):

• Supervised, unsupervised, and semi-supervised learning
• Deep learning architectures and optimization
• Reinforcement learning and sequential decision-making
• Natural language processing and speech technologies
• Computer vision and multimodal learning
• Explainable, interpretable, and trustworthy AI
• Federated learning, privacy-preserving ML, and AI security
• AI for healthcare, education, finance, industry, and public services
• Benchmarking, reproducibility, and evaluation methodologies
• Interdisciplinary AI/ML research with clear scientific contribution