Vol. 2, Issue 1, Part A (2025)

Green synthesis of bimetallic nanoparticles has emerged as an environmentally benign alternative to conventional physicochemical methods, offering reduced toxicity, lower energy consumption, and improved biological compatibility. Among medicinal plants, Madagascar periwinkle (Catharanthus roseus, locally known as Sadāfuli) is rich in alkaloids, flavonoids, and phenolic compounds that can act as natural reducing and stabilizing agents. However, one-pot green synthesis of Ag-Zn bimetallic nanoparticles often suffers from variability in particle size, morphology, and yield due to complex interactions among precursor concentration, extract composition, pH, temperature, and reaction time. These nonlinear and interdependent parameters limit reproducibility and scale-up. The present research explores the integration of machine learning techniques to optimize the one-pot green synthesis of Ag-Zn bimetallic nanoparticles using Sadāfuli extract. Experimental datasets generated from systematic variation of synthesis parameters were modeled using supervised learning algorithms to identify critical factors governing nanoparticle formation and to predict optimal synthesis conditions. Feature importance analysis revealed that metal ion ratio, extract concentration, and reaction pH exert dominant influence on nanoparticle size and stability. The optimized conditions predicted by the model yielded uniformly dispersed Ag-Zn nanoparticles with enhanced crystallinity and reduced polydispersity compared to conventionally optimized samples. The machine learning-guided approach also minimized experimental iterations, reduced material waste, and improved process efficiency. This research demonstrates that coupling green nanotechnology with data-driven optimization provides a robust framework for reproducible synthesis of bimetallic nanoparticles. The findings highlight the potential of machine learning tools in advancing sustainable nanomaterial fabrication, particularly for biologically active nanoparticle systems derived from medicinal plant resources. The proposed strategy is expected to facilitate scalable production of Ag-Zn bimetallic nanoparticles for antimicrobial, catalytic, and biomedical applications while adhering to principles of green chemistry and sustainable development.