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International Journal of Machine Learning, AI & Data Science Evolution [IJMLAIDSE]

The International Journal of Machine Learning, AI & Data Science Evolution (IJMLAIDSE) is a distinguished, peer-reviewed, open-access publication that promotes pioneering research in Artificial Intelligence, Machine Learning, and Data Science. With E-ISSN 3068-5073 and P-ISSN 3068-6932, the journal provides a reliable forum for disseminating impactful and original contributions that push the boundaries of intelligent computing and data-driven innovation.

Published by Apex Academia Press (USA), IJMLAIDSE serves as a global nexus for scholars, scientists, and technologists to share groundbreaking ideas, theoretical advancements, and applied research shaping the next generation of intelligent technologies.

The journal’s vision is to foster innovation through high-quality publications, interdisciplinary collaborations, and transformative discoveries that advance the global AI and data science ecosystem.

  • Journal Name: International Journal of Machine Learning, AI & Data Science Evolution [IJMLAIDSE]
  • E-ISSN: 3068-5073
  • P-ISSN: 3068-6932
  • Frequency: Quarterly
Call for Paper
Call for Papers

IJMLAIDSE invites original manuscripts featuring novel insights in artificial intelligence, data analytics, and computational intelligence for our upcoming editions.

Call for Papers

Submit your best work and be part of a global community redefining the future of intelligent systems and data technologies.

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Editorial Board
Editorial Board

Our editorial panel includes global pioneers in AI, data science, and computer engineering, ensuring academic rigor and innovation in every publication.

Editorial Board

Meet the experts shaping the strategic direction and quality benchmarks of IJMLAIDSE’s research publications.

Meet the Team
Indexing
Indexing

IJMLAIDSE is indexed in multiple international repositories, ensuring visibility and citation for published work across digital libraries and databases.

Indexing

Our inclusion in academic indexes enhances discoverability and global recognition for authors and contributors.

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Types of Articles Published

IJMLAIDSE focuses on publishing innovative and impactful research that advances computational intelligence, data modeling, and AI-driven problem-solving. The journal bridges theoretical frameworks with real-world applications, supporting academic excellence and industrial innovation.

With a strong emphasis on emerging technologies such as deep learning, robotics, natural language understanding, and big data processing, IJMLAIDSE provides a space for interdisciplinary collaboration and knowledge transfer.

We welcome diverse article categories reflecting the rapid evolution of intelligent systems:

  • Original Research Articles: Groundbreaking studies introducing new algorithms, neural architectures, or analytical frameworks in AI and data science.
  • Review Papers: In-depth reviews summarizing progress and identifying future challenges in AI, ML, and data-driven systems.
  • Case Studies: Real-world applications illustrating AI and ML integration across industries like healthcare, education, cybersecurity, and autonomous systems.
  • Short Communications: Rapid reports of emerging concepts, models, or preliminary findings with high potential impact.
  • Editorials & Perspectives: Expert insights exploring technological ethics, responsible AI, and the future of data-centric innovation.

Publication Ethics & Standards

IJMLAIDSE is committed to upholding transparency, academic integrity, and research ethics. All submissions undergo a double-blind peer review and comprehensive plagiarism screening to ensure credibility and originality.

The journal adheres to the ethical guidelines of the Committee on Publication Ethics (COPE), promoting honesty, fairness, and inclusivity in scholarly communication. Our editorial process reflects our dedication to building a trustworthy and sustainable research environment.

Apex Academia Press