Environmental pollution remains a global concern, driven by the widespread release of heavy metals, persistent organic pollutants, microplastics, and endocrine-disrupting chemicals. Traditional detection and remediation technologies often fall short due to high costs, slow response times, and limitations in real-time field applicability. Recent progress in materials science has enabled the development of biosensor-based systems and green remediation strategies, offering more sustainable, efficient, and field-deployable solutions. This chapter explores integrated approaches that combine biological materials—such as plant endophytes and enzyme systems—with nanomaterial-based biosensors for real-time environmental monitoring. Endophytes, known for their metal chelation, enzymatic activity, and stress tolerance, serve dual roles in pollutant detection and remediation. Coupled with green synthesis of nanoparticles and phytoremediation techniques, these biological systems contribute to low-impact, resource-efficient cleanup processes. Advances in material characterization, nanostructure design, and surface engineering have improved biosensor sensitivity and selectivity across complex environmental matrices. Additionally, computational tools such as life cycle assessment (LCA), predictive modeling, and machine learning aid in optimizing biosensor performance and assessing long-term environmental impacts. The chapter highlights how the convergence of experimental innovations and computational methodologies in materials science enables the design of smart, multifunctional systems for pollution management. Emphasis is placed on sustainability, adaptability, and real-world applicability, aligning with the broader goals of circular economy and green engineering. This interdisciplinary perspective supports the ongoing transition toward environmentally responsible technologies within the framework of materials science and engineering.