**1. Introduction**

From the day humans started invading this planet, Earth has been crammed with numerous toxic pollutants from multiple sources. Advance scientific technology has given rise to multiple

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tools to reduce pollutants in different ways, and bioremediation is considered to be the best way to neutralise polluted environments on Earth [1, 2]. In this genomic era, metagenomic approaches have been developed and are known as effective methods of removing various kinds of pollutants [3, 4]. Metagenomics is a strategic approach of analyzing microbial communities at a genomic level. This provides a glimpse of the microbial community view of "Uncultured Microbiota". Recent studies suggest that microbial communities are the potential alternatives to eliminate toxic contaminants from our environment [5-8]. The term metage‐ nomics was coined by Jo Handelsman et al. in 1998. They have accessed the collective genomes and the biosynthetic machinery of soil microflora during a study of cloning the metagenome [9]. Bioremediation has always been adapting new advances in science and technology for establishing better environments. Compared with the previous years, there has been a gradual increase of interest in metagenomics-based bioremediation studies [10-12]. These studies can prove that metagenomics is one of the best adaptations of bioremediation leading to the establishment of a pure nontoxic environment.

In this chapter, we discussed recent approaches of metagenomics in bioremediation with the help of recent multiple case studies. Preliminarily, we explained the methodology behind metagenomic analysis, starting from the sample screening and ending up with metagenomic analysis with respect to bioremediation. Metagenomic bioremediation reviews and extracts microbial communities applying their extensive biochemical pathways in degrading toxic pollutants. A part of our study aims to emphasize multiple case studies of metagenomic applications on air, water, and soil contaminations. Our analysis provided a topic-specific landscape with respect to metagenomic bioremediation of water contaminations, soil contam‐ inations, and followed by air contaminations. The following part of our study focuses on recently developed sequence and function-based metagenomic strategies to analyze metage‐ nomes from contaminated environments. In addition to this, our study explains the highly prevalent metagenomes derived from metagenomic communities which are also highly capable of degrading contaminations and toxins in the environment. Finally, we provided a landscape view of multiple bioinformatic tools used in the processing and analysis of meta‐ genomic bioremediation data.
