Strategic Framework for Sustainable Nanoparticle Synthesis: From Physical and Green Routes to Machine Learning Optimization

Nanoparticles are involved in improving healthcare, energy, and the environment due to their unique physicochemical properties. The purpose of this review is to compare some of the most prevalent nanoparticle synthesis strategies—considering physical, chemical, green, and hybrid types of synthesis—and assess the differences in each method's scalability, resistance, and application. This review presents a structured decision-making method that diverges from the published literature by considering application needs, sustainability, and production sustainability to inform the selection of the best nanoparticle synthesis strategy for a given application, taking into account factors such as cost, speed, reproducibility, and functional control. The primary focus of this review is on hybrid synthesis methods that leverage the advantages of various routes. Another significant development discussed in this review is the emergence of machine learning (ML) for predictive parameter tuning and real-time synthesis optimization, allowing synthesis to be optimally discussed both predictively and reactively. By combining conventional approaches with data science, this review aims to help researchers and industries develop safer, more sustainable, and ultimately more innovative systems for nanoparticle synthesis.