Phytochemicals have emerged as powerful anticancer agents owing to their diverse mechanisms, ranging from apoptosis induction and signaling inhibition to modulation of the tumor microenvironment. However, traditional screening methods are time-consuming, resource-intensive, and poorly suited to the complex nature of plant-derived compounds. Recent advances in automated real-time screening platforms have revolutionized the discovery process, enabling high-throughput, cost-efficient, and reproducible assessment of phytochemical efficacy. These platforms integrate robotics, microfluidics, biosensors, and machine learning to process tens of thousands of compounds per day while capturing dynamic cellular responses in real time. Lab-on-chip systems, droplet-based microfluidics, and electric-impedance biosensors allow for miniaturized, physiologically relevant assays that provide high-resolution phenotypic and metabolic insights. AI-powered screening pipelines and pharmacoinformatics tools now allow for the prediction of binding affinities, drug-likeness, and synergistic interactions, thus accelerating lead identification and optimization. Case studies involving curcumin, resveratrol, and novel compounds like RPS20 inhibitors highlight the synergy between computational and wet-lab approaches. Despite these advances, challenges remain, such as solubility variability, data overload, and infrastructure costs. Nonetheless, the future lies in integrating real-time screening with patient-derived models and multi-omics data for personalized phytochemical therapy. This chapter provides a comprehensive overview of these evolving technologies, their current applications, limitations, and transformative potential in natural product-based cancer drug discovery.