eng Oriental Scientific Publishing Company Biosciences Biotechnology Research Asia 0973-1245 2026-10-10 23 3 60139 article Jonathan Josué Cid-Galiot 1 Alberto Alfonso Aguilar-Lasserre 1 Daniel Cuervo-Zanatta 2 José Ernesto Domínguez-Herrera 3 Armin Trujillo-Mata 4 Graduate Studies and Research Division, Instituto Tecnológico de Orizaba, Tecnológico Nacional de México, Orizaba, Veracruz, México Department of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, University of Vienna, Vienna, Austria Industrial Maintenance Engineering, Technological University of the Center of Veracruz, Cuitlahuac, Veracruz, México Industrial Engineering, Anahuac University, Córdoba, Veracruz, México Artificial intelligence (AI) is transforming preclinical research in Alzheimer’s disease (AD), particularly through its integration with Murine experimental models (MEMs) for early detection, monitoring, and therapeutic evaluation. However, current research remains fragmented, with limited standardization and insufficient integration across methodological and translational domains. This systematic literature review (SLR) analyzes 221 peer-reviewed studies published between 2009 and 2025, focusing on AI applications in preclinical AD models. The study takes a closer look at a wide range of AI techniques, from machine learning and deep learning to computer vision, natural language processing, and generative models, mapping out how each is used at various stages of AD and within different experimental approaches. In examining these studies, the results reveal a strong predominance of unimodal approaches, limited use of multimodal data fusion, and significant variability in validation protocols, which collectively hinder reproducibility and translational impact. Additionally, the analysis identifies key gaps in real-time monitoring, explainability, and integration of heterogeneous datasets. Based on these findings, this work proposes a conceptual framework for AI-assisted preclinical pipelines that integrates multimodal data, adaptive learning systems, and translational validation strategies. This framework is designed to enhance potential implications, such as the clinical relevance of preclinical AD research, to facilitate the development of scalable, precision medicine-oriented diagnostic and therapeutic solutions. https://www.biotech-asia.org/vol23no3/artificial-intelligence-driven-preclinical-frameworks-for-alzheimers-disease-a-systematic-review-of-murine-other-animal-and-non-animal-models-biomarkers-monitoring-and-translational-gap/ Alzheimer's disease (AD); Artificial Intelligence (AI); Murine Experimental models (MEMs); Preclinical Frameworks (PF); Systematic Literature Review (SLR)