Machine Learning (ML) and Deep Learning (DL) in Healthcare and Pharmaceutical Innovation: Alzheimers Disease Diagnosis and Drug Discovery

Machine Learning (ML) together with Deep Learning (DL) function as vital technological tools which drive healthcare and pharmaceutical development through their ability to analyze complex biomedical data at scale. The growing amount of electronic health records, together with medical imaging, genomic databases, and clinical trial repositories, requires computational approaches which produce accurate predictions and clinical decision support. The research investigates all machine learning (ML) and deep learning (DL) applications which appear in healthcare and pharmaceutical development for neurodegenerative diseases including Alzheimer's disease. The healthcare industry experiences major improvements in disease detection through early stages because of two ML systems which combine deep learning with convolutional neural networks and recurrent neural networks. Transformer-based architectures for medical image analysis and patient risk assessment. The methods enable Alzheimer's disease diagnosis at an early stage through neuroimaging analysis and cognitive assessment modelling and biomarker-based prediction approaches. The system enables fast disease detection which enables medical professionals to start treatment without delay while they can watch the disease progression. Pharmaceutical research benefits from machine learning (ML) and deep learning (DL) because these technologies accelerate drug discovery and development through their ability to perform virtual screening, drug-target interaction prediction, toxicity evaluation, and pharmacokinetic and pharmacodynamic optimization. Researchers actively search for new treatment options to fight Alzheimer's disease. The combination of ML with DL, big data analytics, Internet of Medical Things platforms, and cloud-based infrastructures enables better patient monitoring through real-time surveillance. This helps create personalized treatment approaches. The new technologies have not solved the problem of clinical application because medical data remains unstructured and deep learning models stay difficult to interpret, and there are ethical problems, and medical professionals must adhere to established rules. The paper presents current advancements together with their main applications and challenges while it predicts future trends, which include explainable artificial intelligence and precision medicine to create reliable, scalable systems.