GPCRs (G-protein-coupled receptors) are membrane-bound proteins that play an important role in a variety of physiological processes and pathophysiological conditions. Among the pharmacological agents approved by the US Food and Drug Administration, they represent more than forty percent of the major targets. In spite of these challenges, structural analysis and drug design aimed at the GPCRs face some hard challenges, among which is finding high-resolution structures due to their lipid-embedded nature. Cryogenic electron microscopy (cryoEM) has reached many new developments in recent years, enabling a better understanding of how GPCRs work and allowing detailed examinations of GPCR-G-protein complexes. In recent years, cryo-EM combined with stabilizing proteins such as nanobodies and mini-G-proteins has made significant advances in receptor stabilization and allowed a deeper understanding of the structure. Machine learning techniques have also become one of the most promising approaches to accelerate GPCR-based drug discovery. With the help of deep learning systems like DeepGPCR_BC and DeepGPCR-RG, which operate with nonstructural interaction data, it is possible to represent ligands and binding sites in the form of graphs, which effectively enhances the speed and accuracy of predictions. Independent testing has demonstrated these models to be more effective than traditional docking programs, and the large-scale virtual screening programs have potential. In such ways, the application of the latest technology, single-molecule fluorescent resonance energy transfer (smFRET), and organ-on-a-chip is part of the knowledge on GPCR activity in physiological conditions. With such modalities, the interactions between the receptors in real time can be followed, and pharmacological screening can be performed in conditions that are more likely to represent in vivo conditions. The current GPCR drug discovery successes are also summarized, such as technologies in structural determination, machine-learning interventions, and microphysiological systems.