The Coronavirus disease (COVID-19) caused by the virus SARS-CoV-2 has become a global pandemic in a very short time span. Currently, there is no specific treatment or vaccine to counter this highly contagious disease. There is an urgent need to find a specific cure for the disease and global efforts are directed at developing SARS-CoV-2 specific antivirals and immunomodulators. Ayurvedic Rasayana therapy has been traditionally used in India for its immunomodulatory and adaptogenic effects, and more recently has been included as therapeutic adjuvant for several maladies. Amongst several others, Withania somnifera (Ashwagandha), Tinospora cordifolia (Guduchi) and Asparagus racemosus (Shatavari) play an important role in Rasayana therapy. The objective of this study was to explore the immunomodulatory and anti SARS-CoV2 potential of phytoconstituents from Ashwagandha, Guduchi and Shatavari using network pharmacology and docking. The plant extracts were prepared as per ayurvedic procedures and a total of 31 phytoconstituents were identified using UHPLC-PDA and mass spectrometry studies. To assess the immunomodulatory potential of these phytoconstituents an in-silico network pharmacology model was constructed. The model predicts that the phytoconstituents possess the potential to modulate several targets in immune pathways potentially providing a protective role. To explore if these phytoconstituents also possess antiviral activity, docking was performed with the Spike protein, Main Protease and RNA dependent RNA polymerase of the virus. Interestingly, several phytoconstituents are predicted to possess good affinity for the three targets, suggesting their application for the termination of viral life cycle. Further, predictive tools indicate that there would not be adverse herb-drug pharmacokinetic-pharmacodynamic interactions with concomitantly administered drug therapy. We thus make a compelling case to evaluate the potential of these Rasayana botanicals as therapeutic adjuvants in the management of COVID-19 following rigorous experimental validation.