Nanotechnology and Machine Learning: A Promising Confluence for the Advancement of Precision Medicine
Shuaibu Saidu Musa
a,b
,
Adamu Muhammad Ibrahim
c
,
Muhammad Yasir Alhassan
d
,
Abubakar Hafs Musa
e
,
Abdulrahman Garba Jibo
f
,
Auwal Rabiu Auwal
g,h
,
Olalekan John Okesanya
i,j
,
Zhinya Kawa Othman
k
,
Muhammad Sadiq Abubakar
l
,
Mohamed Mustaf Ahmed
m
,
Carina Joane V. Barroso
n
,
Abraham Fessehaye Sium
o
,
Manuel B. Garcia
p,q,r
,
James Brian Flores
s
,
Adamu Safiyanu Maikifi
t
,
M.B.N. Kouwenhoven
u
,
Don Eliseo Lucero-Prisno
v,w,x
a School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
b Department of Nursing Science, Ahmadu Bello University, Zaria, Nigeria
c Department of Immunology, School of Medical Laboratory Science, Usmanu Danfodiyo University, Sokoto, Nigeria
d Department of Public Health, Symbiosis Institute of Health Sciences, Symbiosis International (Deemed University), Pune, India
e Graduate School Nanoscience and Technology, Chulalongkorn University, Bangkok, Thailand
f Department of Industrial Pharmacy, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok Thailand
g Department of Biochemistry, Chulalongkorn University Bangkok, Thailand
h Department of Biochemistry Aliko Dangote University of Science and Technology, Wudil, Nigeria
i Department of Public Health and Maritime Transport, University of Thessaly, Volos, Greece
j Department of Medical Laboratory Science, Neuropsychiatric Hospital, Aro, Abeokuta, Ogun State, Nigeria
k Department of Pharmacy, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq
l Department of Public Health, Ahmadu Bello University, Zaria, Nigeria
m Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia
n Bukidnon State University, Malaybalay City, Bukidnon, Philippines
o Department of Obstetrics and Gynecology, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
p University of the Philippines Diliman, Quezon City, Philippines
q FEU Institute of Technology, Manila, Philippines
r Korea University, Seoul, South Korea
s College of Computer Studies and Information Technology, Southern Leyte State University, Sogod, Southern Leyte, Philippines
t Department of Pharmaceutics and Industrial Pharmacy, Chulalongkorn University, Bangkok, Thailand
u Department of Physics, Xi'an Jiaotong-Liverpool University, Suzhou, China
v Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom
w Research and Innovation Office, Biliran Province State University, Naval, Biliran Province, Philippines
x Center for Research and Development, Cebu Normal University, Cebu City, Philippines
Abstract: The fusion of molecular-scale engineering in nanotechnology with machine learning (ML) analytics is reshaping the field of precision medicine. Nanoparticles enable ultrasensitive diagnostics, targeted drug and gene delivery, and high-resolution imaging, whereas ML models mine vast multimodal datasets to optimize nanoparticle design, enhance predictive accuracy, and personalize treatment in real-time. Recent breakthroughs include ML-guided formulations of lipid, polymeric, and inorganic carriers that cross biological barriers; AI-enhanced nanosensors that flag early disease from breath, sweat, or blood; and nanotheranostic agents that simultaneously track and treat tumors. Comparative insights into Retrieval-Augmented Generation and supervised learning pipelines reveal distinct advantages for nanodevice engineering across diverse data environments. An expanded focus on explainable AI tools, such as SHAP, LIME, Grad-CAM, and Integrated Gradients, highlights their role in enhancing transparency, trust, and interpretability in nano-enabled clinical decisions. A structured narrative review method was applied, and key ML model performances were synthesized to strengthen analytical clarity. Emerging biodegradable nanomaterials, autonomous micro-nanorobots, and hybrid lab-on-chip systems promise faster point-of-care decisions but raise pressing questions about data integrity, interpretability, scalability, regulation, ethics, and equitable access. Addressing these hurdles will require robust data standards, privacy safeguards, interdisciplinary R&D networks, and flexible approval pathways to translate bench advances into bedside benefits for patients. This review synthesizes the current landscape, critical challenges, and future directions at the intersection of nanotechnology and ML in precision medicine.