The mass pollution of plastics in the world is having a detrimental impact on both marine biology and human health. While there are efforts to clean and analyze plastics with the help of the community, these plastics often break down into smaller particles referred as microplastics, which are more difficult to study and remove. Numerous studies are coming out with the attempt to detect and identify these microplastics with hopes for future improvements to reduce this global hazard. However, a main issue with current research is the lack of standardization in the testing and sampling of microplastics. Most studies involve collecting samples from different bodies of water and then transporting them to a laboratory for treatment, filtering, and examination using various methods to classify the type of plastic and gather information about its size and quantity. This process can take a large amount of time and is separated primarily into stages: sampling, treatment, classification, and detection. This thesis will explore different research techniques that incorporate some of these stages and proposes a portable system that can sort particles using mechanical filtration, detect particles using the You Only Look Once (YOLO) convolutional neural network, and classify particles using Raman spectroscopy. The system will be deployed in the field with a focus in reducing the amount of time and labor involved with the process of microplastic analysis by combining techniques into one system. The project that will be presented in this thesis is a work in progress and research is still being implemented and advanced within the current system. Possible improvements are addressed for future students to expand on. The goal of this work is to lay a foundation for a sustainable, versatile system for use in various environments and over extended periods of time. This thesis will present a literature review on the current work being done in the field as well as the techniques used, and each individual part of the current deployed system will be explained.