This study introduces a novel method called Smart Polarization and Spectroscopic Holography (SPLASH) for real-time identification of microplastics (MPs). SPLASH combines polarization and spectroscopic holography to simultaneously capture polarization, holographic, and texture features, enabling accurate and efficient MP identification without the need for a physical spectroscopic system. The method uses a Stokes polarization mask (SPM) to capture four polarization states in one shot and leverages machine learning algorithms for automatic classification. The system demonstrates high accuracy, with an area under the curve (AUC) value of over 0.8 and less than 0.05 variance, achieving effective discrimination of MPs based on their molecular structure and composition. The study highlights the importance of MP identification in assessing environmental and health risks, as MPs can be ingested by organisms and cause various health issues. Traditional methods for MP identification, such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM), are limited by factors like environmental weathering and aging. Spectroscopic methods, while providing qualitative analysis, suffer from weak signals and long processing times. The proposed SPLASH system addresses these limitations by providing a non-contact, non-invasive, and efficient method for MP identification. The system was tested on various MPs, including polyethylene terephthalate (PET), polypropylene (PP), polycarbonate (PC), and polyvinyl chloride (PVC), as well as natural particles such as the young root of plant T.S., Chlorella, and Daphnia magna. The results show that SPLASH can effectively distinguish between MPs and natural particles based on their polarization, holographic, and texture features. The system's performance was evaluated using receiver operating characteristic (ROC) curves and AUC values, demonstrating its high accuracy and reliability. The study also discusses the potential applications of SPLASH in environmental monitoring, including MP source identification, long-term water pollution monitoring, and the assessment of MP risks to ecosystems and human health. The system's compact design and integration with machine learning algorithms make it suitable for various environmental applications. Future work aims to develop high-throughput microfluidic systems for non-contact, quick MP quantification and to explore the system's potential in industrial and medical material analysis.This study introduces a novel method called Smart Polarization and Spectroscopic Holography (SPLASH) for real-time identification of microplastics (MPs). SPLASH combines polarization and spectroscopic holography to simultaneously capture polarization, holographic, and texture features, enabling accurate and efficient MP identification without the need for a physical spectroscopic system. The method uses a Stokes polarization mask (SPM) to capture four polarization states in one shot and leverages machine learning algorithms for automatic classification. The system demonstrates high accuracy, with an area under the curve (AUC) value of over 0.8 and less than 0.05 variance, achieving effective discrimination of MPs based on their molecular structure and composition. The study highlights the importance of MP identification in assessing environmental and health risks, as MPs can be ingested by organisms and cause various health issues. Traditional methods for MP identification, such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM), are limited by factors like environmental weathering and aging. Spectroscopic methods, while providing qualitative analysis, suffer from weak signals and long processing times. The proposed SPLASH system addresses these limitations by providing a non-contact, non-invasive, and efficient method for MP identification. The system was tested on various MPs, including polyethylene terephthalate (PET), polypropylene (PP), polycarbonate (PC), and polyvinyl chloride (PVC), as well as natural particles such as the young root of plant T.S., Chlorella, and Daphnia magna. The results show that SPLASH can effectively distinguish between MPs and natural particles based on their polarization, holographic, and texture features. The system's performance was evaluated using receiver operating characteristic (ROC) curves and AUC values, demonstrating its high accuracy and reliability. The study also discusses the potential applications of SPLASH in environmental monitoring, including MP source identification, long-term water pollution monitoring, and the assessment of MP risks to ecosystems and human health. The system's compact design and integration with machine learning algorithms make it suitable for various environmental applications. Future work aims to develop high-throughput microfluidic systems for non-contact, quick MP quantification and to explore the system's potential in industrial and medical material analysis.