Multiphasic processes underpin nearly every aspect of modern life; from the food we eat and the clothes we wear to the medicines that sustain us and the energy systems that power our world. This thesis presents three contributions to the advancement of multiphasic processes by developing intelligent control, scalable processing, and reactor design. First, artificial intelligence is applied to droplet and bubble microfluidics through convolutional neural networks (CNNs) in the form of soft-sensors. Coupled with proportional–integral–derivative (PID) feedback loops, these sensors enable autonomous control of droplet size and flow regimes, maintaining stable operation under disturbances and recovering from failure modes without operator intervention. The framework is further extended to complex geometries, including double emulsions and anisotropic hydrogel rods, demonstrating adaptability across several multiphase microfluidic systems. Second, a sliding film evaporator (SFE) is developed as a continuous method for solvent removal from PLGA emulsions. By spreading emulsions into thin films across temperature-controlled channels, the SFE accelerates chloroform removal while maintaining particle uniformity, overcoming the inconsistency and scalability limitations of traditional evaporation techniques. Finally, bubble-mediated transesterification reactors are developed for biodiesel synthesis from algae-derived triglycerides. Methanol microbubbles enhance interfacial mass transfer and lower the apparent activation energy, enabling >95% conversion under moderate conditions. Kinetic studies coupled with ASPEN PLUS simulations demonstrate reactor scalability, while highlighting the importance of diffuser design and continuous glycerol removal. Together, these contributions provide a pathway to the continual improvement of multiphasic process engineering.