Phytochemicals, natural compounds derived from plants, have a long history in traditional medicine and are increasingly being explored for drug discovery. Recent advancements in analytical techniques and computational methods have made it easier to identify bioactive leads from natural compounds. Computational tools such as molecular docking, QSAR modeling, machine learning, and network pharmacology are crucial for predicting potential targets and guiding experimental validation. LC-MS and LC-NMR accelerate compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, providing valuable insights into how phytochemicals interact with biological targets. Traditional medicine, influenced by indigenous knowledge systems, has utilized phytochemicals for centuries. Plant-based remedies have shaped modern pharmacology, and ethnopharmacology seeks to understand how specific phytochemicals function in traditional healing methods. Phytochemicals have shown promise in treating various diseases, including viral infections, cancer, and antimicrobial resistance. Examples include apomorphine, arteether, galantamine, and tiotropium, which are approved for treating Parkinson's disease, malaria, Alzheimer's, and asthma, respectively. In modern drug discovery, phytochemicals are being used as antivirals, cancer combination therapies, and antimicrobials. For instance, phytochemicals like saponins and curcumin have shown antiviral properties against viruses such as SARS-CoV-2. In cancer treatment, phytochemicals can act on multiple biological pathways, enhancing overall treatment efficacy and reducing drug resistance. Antimicrobial activities of phytochemicals are also being explored to combat antibiotic resistance. Computational approaches, including virtual screening, molecular docking, and machine learning, have significantly advanced phytochemical drug discovery. These tools help predict binding modes, optimize compounds, and identify potential targets. For example, molecular docking has been used to assess the interaction of phytochemicals with SARS-CoV-2 targets, and machine learning algorithms have been employed to predict bioactivity data and identify novel glucocorticoid receptor antagonists. Despite the benefits, challenges such as the complexity and variability of phytochemical compositions, extraction difficulties, low bioavailability, and safety concerns remain. However, ongoing research and technological advancements are addressing these issues, making phytochemicals a promising source for novel therapeutic agents.Phytochemicals, natural compounds derived from plants, have a long history in traditional medicine and are increasingly being explored for drug discovery. Recent advancements in analytical techniques and computational methods have made it easier to identify bioactive leads from natural compounds. Computational tools such as molecular docking, QSAR modeling, machine learning, and network pharmacology are crucial for predicting potential targets and guiding experimental validation. LC-MS and LC-NMR accelerate compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, providing valuable insights into how phytochemicals interact with biological targets. Traditional medicine, influenced by indigenous knowledge systems, has utilized phytochemicals for centuries. Plant-based remedies have shaped modern pharmacology, and ethnopharmacology seeks to understand how specific phytochemicals function in traditional healing methods. Phytochemicals have shown promise in treating various diseases, including viral infections, cancer, and antimicrobial resistance. Examples include apomorphine, arteether, galantamine, and tiotropium, which are approved for treating Parkinson's disease, malaria, Alzheimer's, and asthma, respectively. In modern drug discovery, phytochemicals are being used as antivirals, cancer combination therapies, and antimicrobials. For instance, phytochemicals like saponins and curcumin have shown antiviral properties against viruses such as SARS-CoV-2. In cancer treatment, phytochemicals can act on multiple biological pathways, enhancing overall treatment efficacy and reducing drug resistance. Antimicrobial activities of phytochemicals are also being explored to combat antibiotic resistance. Computational approaches, including virtual screening, molecular docking, and machine learning, have significantly advanced phytochemical drug discovery. These tools help predict binding modes, optimize compounds, and identify potential targets. For example, molecular docking has been used to assess the interaction of phytochemicals with SARS-CoV-2 targets, and machine learning algorithms have been employed to predict bioactivity data and identify novel glucocorticoid receptor antagonists. Despite the benefits, challenges such as the complexity and variability of phytochemical compositions, extraction difficulties, low bioavailability, and safety concerns remain. However, ongoing research and technological advancements are addressing these issues, making phytochemicals a promising source for novel therapeutic agents.