According to the President of the United Nations, AI holds enormous promise for accelerating progress towards numerous United Nations Sustainable Development Goals (SDGs). This paper focuses on various applications of technologies such as artificial neural networks (ANN) and deep learning (DL) in the development of pharmaceutical solid dosage forms. DL is a subset of machine learning (ML) that utilizes extensive experimental data to learn through advanced methods like artificial neural networks. ANNs can analyze patient data to generate customized drug delivery regimens based on genetic and medical histories. A range of AI technologies, including neural networks, fuzzy logic, and evolutionary algorithms, are employed for creating solid dosage formulations. Support vector machine (SVM), a unique machine learning approach, was applied to predict oral drug absorption in humans using descriptors derived from chemical structure. ANNs are capable of forecasting drug dissolution profiles, predicting particle flowability, anticipating storage stability, and designing stable dosage forms. The study evaluates the accuracy of ANN, CNN, and SVM in oral solid dosage forms, drug release prediction, virtual screening, and pharmacokinetics/pharmacodynamics in drug delivery systems.