Background: The therapeutic properties of phytochemicals found in medicinal plants, as well as in dietary plants and herbs, have had a significant impact on the development of several herbal remedies for a range of ailments. Amalaki churna, a well-known ayurvedic composition, has been traditionally used to treat a number of disorders and has been shown to have anti-inflammatory, anti-diabetic, and anti-cancer properties, among others. The presence of large number of phytochemicals in this ayurvedic formulation seems to be responsible for its diverse actions. However, little is known about the precise molecular interaction between phytochemicals and their protein targets. This study puts forward a methodical and structured approach to determine the targets for the phytochemicals recovered from the aqueous extract of Amalaki churna and to establish the phytochemical-protein cross talk occurring at the molecular level. Materials and Methods: Various phytochemicals were identified from AMCAE using spectroscopic techniques such as HPLC, NMR (1H, 13C, and 2D NMR), and UPLC-MS analysis. The phytochemical-protein cross talk was discovered utilizing network-based pharmacology, with phytochemicals serving as core nodes. Seven hub proteins were considered most important from the string network of the protein targets using variable parameters such as Degree Centrality (DCY), Closeness Centrality (CCY) and Betweenness Centrality (BCY). Molecular docking and molecular dynamic simulations were carried out to ascertain the stability of the best docked phytochemical-protein complexes. The in silico findings were further validated using c-Src kinase protein as a model. Results: In this study, we have fractionated and identified 8 phytochemicals from Amalaki churna. We were able to find a total of 387 protein targets from the Drug bank and Binding DB by using structural similarity search. A network with 273 nodes and 417 edges that was acquired from Drug Bank demonstrated the substantial cross talk between phytochemicals and their protein targets. In a similar manner, phytochemical similarity search from Binding data bank resulted in a protein-phytochemical network with 143 nodes and 275 edges. Further, seven hub proteins with most interconnectedness were selected as the top most protein targets of the phytochemicals. Molecular modelling and docking experiments show that phytochemicals fit well into the target proteins' active sites. Molecular dynamic studies were used to further demonstrate the durability and stability of the protein phytochemical complexes. Phytochemicals were also shown to alter cell cycle regulation, disrupt cell survival pathways, limit cell migratory capacity, and induce apoptosis. The disruption of many cellular pathways was demonstrated using c-Src kinase as a model, in which down-regulation of the tyrosine kinase by phytochemicals led in the disruption of its downstream proteins such as Akt1, cyclin D1 and vimentin. Conclusion: Network analysis, followed by molecular docking, molecular dynamics modelling, and in vitro studies, clearly underline the importance of protein-phytochemical cross-talk.