The conventional method of drug discovery and development, which depends on lengthy clinical trials and intensive laboratory testing, is expensive and time-consuming. Artificial intelligence, particularly deep learning, is transforming this domain by enhancing predictive accuracy and accelerating drug development stages. This work investigates the use of deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in drug-target interaction prediction, chemical structure optimization, and drug candidate identification. It prepares a step-by-step method, covering data preprocessing, model structure, and performance evaluation. Experimental results show that deep learning can greatly increase predictive accuracy when compared with conventional methods, so less effort and time are needed in future drug discovery work. It highlights how advances in AI could transform the future of pharmaceutical research, based as they are on two major trends: speeding up development timelines and raising success rates. The study stresses the importance of interdisciplinary cooperation between computational scientists and pharmacologists to fully use AI in both drug discovery and clinical applications.