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@article{207604,
        author = {Dr. Kiran Bhagnani},
        title = {Architectural Evolution and Methodological Advancements in Artificial Intelligence and Machine Learning: A Comprehensive Survey and Future Horizons},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1783-1786},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207604},
        abstract = {Artificial Intelligence (AI) and Machine Learning (ML) have undergone a structural paradigm shift, evolving from early rule-based statistical systems into high-dimensional, self-attention-driven multimodal systems and dynamic Agentic AI frameworks. This paper presents a rigorous synthesis of AI and ML theoretical foundations, architectural progressions, and state-of-the-art developments. We formally analyze supervised, unsupervised, and reinforcement learning frameworks, detailed mathematical mechanisms underlying modern deep neural architectures, and recent advances in multimodal integration, Parameter-Efficient Fine-Tuning (PEFT), and autonomous multi-agent workflows. Furthermore, we examine critical technical bottlenecks—including computational energy complexity, mechanistic interpretability, data governance, and hallucination bounds—and outline strategic avenues for future research.},
        keywords = {Artificial Intelligence, Machine Learning, Deep Learning, Transformer Architecture, Agentic AI, Multimodality, Parameter-Efficient Fine-Tuning, Mechanistic Interpretability.},
        month = {August},
        }