Microwave sensors are popular for characterizing materials non-invasively. Traditionally, designing these sensors and processing the data obtained from them requires a lot of trial and error and complex processing algorithms. Machine learning (ML) can offer a faster and more efficient way to improve the material characterization process through optimized sensor shapes, improved sensitivity, and more efficient and accurate material properties extraction. This article reviews how ML techniques are used in microwave dielectric sensing to improve sensor design, data processing, and measurement reliability. It also explores current challenges and opportunities, highlighting how ML can help sensors adapt to changing conditions and optimize performance in various situations. Overall, the research shows the important role ML can play in making microwave dielectric sensing more efficient, accurate, and scalable.