AI-Driven Environmental Pollution Detection Using Zinc Oxide Nanoparticles Synthesized from Ulva Intestinalis

Ulva‑mediated green synthesis of zinc oxide (ZnO) nanomaterials offers a sustainable pathway for next‑generation environmental monitoring platforms that overcome the limitations of conventional laboratory‑bound pollutant analysis. This review critically examines the role of Ulva intestinalis as a biofactory for ZnO nanoparticles and connects its unique phytochemical profile to mechanistic aspects of nanoparticle formation, surface functionalization, and performance in sensing applications. The discussion begins with the changing global pollution landscape and the constraints of chromatographic–mass spectrometric techniques, motivating a shift toward distributed nanosensor systems capable of real‑time detection of complex contaminant mixtures. The principles of green chemistry are then related to Ulva‑derived extracts, highlighting how ulvan, proteins, and associated polysaccharides act as reducing, chelating, and capping agents that drive nucleation, growth, and stabilization of wurtzite‑phase ZnO with tailored morphology, porosity, and surface chemistry. Subsequent sections link physicochemical features such as crystal structure, defect states, bandgap, and bio‑organic corona to chemiresistive gas sensing, photocatalytic degradation, and electrochemical detection of heavy metal ions in environmental matrices. Particular emphasis is placed on the integration of artificial intelligence and machine learning for signal preprocessing, feature extraction, pattern recognition, and drift compensation, supporting robust pollutant classification and quantification under variable humidity and multi‑pollutant conditions. Finally, the review outlines the incorporation of green‑synthesized ZnO into IoT and AIoT architectures, addressing biosafety, ecotoxicological considerations, scalability of algal synthesis, and prospects for self‑powered piezo‑phototronic devices within smart environmental monitoring ecosystems.