eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-07-10 18 August Spl Edition 66680 article Mansi Butola 1 Bhawna Chhabra 2 Abhishek Gupta 3 Meenu Chaudhary 4 Sanjeev Kumar Shah 5 Praveen Kumar 6 Department of Pharmaceutics, Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, Uttarakhand, India Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Noida, Uttar Pradesh, India Department of Electrical Engineering, Dr. B.R. Ambedkar Institute of Technology, Sri Vijaya Puram, A and N Islands Department of Chemistry, School of Pharmaceutical Sciences, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India Department of Computer Sciences, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand, India Department of Chemistry, Himalayan Institute of Pharmacy and Research, Dehradun, Uttarakhand, India The integration of Artificial Intelligence (AI) and neuroscience is transforming our comprehension of the brain, revealing new opportunities in study, diagnosis, and treatment. The article explores how advanced AI methods, including deep learning and neuromorphic computing, are transforming neuroscience by facilitating the analysis of complex brain datasets. This article also seeks to educate healthcare professionals on pertinent topics of AI, that is, machine learning (ML) and deep learning, to explore the development of AI-powered technological innovation, and to clarify how ML can revolutionize the treatment of neurological disorders. This article explores the unsupervised aspects of ML and its possible use in precision neurology to increase patient outcomes. We have talked about various types of current AI, past research, results, advantages and limitations of AI, efficient accessibility, and the future of AI, keeping in view the current burden of neurological diseases. The intelligent device system for tremor monitoring and phenotyping of tremors is intended to improve results of deep brain stimulation. It encompasses uses for the evaluation of fine motor skills, AI-based electroencephalogram analysis for the diagnosis of epilepsy and psychological non-epileptic seizures, outcome prediction of seizure surgeries, detection of patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), detection of complex algorithm patterns in neuroimaging to classify cognitive impairment, discrimination and classification of concussion phenotypes, smartwatches to monitor atrial fibrillation to prevent stroke, and prognosis prediction in dementia. These are various situations of experimental uses of AI in the neurology practice. Despite the apparent limitations of AI, the overwhelming consensus among several national researches is that such new technology could improve the prediction of neurological illnesses and, accordingly, should be incorporated into the medical practice. AI enables the examination of medical information for disease prevention, diagnosis, monitoring of patients, and creation of new procedures, as well as assisting doctors in handling large amounts of data with greater accuracy and efficiency. https://biomedpharmajournal.org/vol18octoberspledition/artificial-intelligence-as-an-emerging-technique-in-the-contemporary-management-of-neurological-disorders/ Artificial intelligence Epilepsy Neurological disorders Seizures Technology