Plants possess a vast spectrum of specialized metabolites with various chemical structures and biological functions responsible for plant fitness, adaptability, and ecosystem services. They are also a central source of commercially valuable pharmaceutical, cosmetic, and food phytochemicals. Despite significant enhancements in plant metabolomics research in the last two decades, only a small proportion of the total expected pool of secondary metabolites (SMs) has been evaluated and identified. This is due to their structural and functional diversity, qualitative and quantitative variability with species, cells, developmental stages, environmental conditions, and restricted occurrence in limited plants. A better understanding of plant secondary metabolomics has the potential to revolutionize research in plant biology and agriculture. Moreover, the discovery novel drugs or lead compounds has huge biomedical and financial implications for the pharmaceutical industry. Targeted and untargeted approaches are commonly applied for SM evaluations. Targeted metabolic analysis selects and quantifies specific metabolites relevant to a biological process or metabolic pathway. In contrast, the untargeted metabolic analysis allows unbiased investigation of a global snapshot of all measurable metabolites present in plant extracts without prior knowledge. Targeted approaches have limitations in exploring new metabolites that lack prior knowledge, while integrated approaches have limitations like data analysis complexity, erroneous identification, lower sensitivity, and reliance on instrument accuracy. Qualitative and quantitative analysis of plant SMs and their identification has evolved quickly due to new analytical techniques that have improved sensitivity, data acquisition efficiency, accuracy, and time requirements. Classical chromatography, mass spectrometry, and NMR spectrometry techniques are replaced by various hyphenated technologies, like GS-MS and LC-MS, for better identification and quantification of SMs. Developing bioinformatic tools, biostatistics tools, and global databases are essential for maximizing compound annotation and serving functions. This chapter highlights various classical and advanced analytical techniques, their limitations, recent advancements and innovations, and prospects of SM identification.