Ecosystem monitoring is crucial amid growing environmental challenges like habitat loss, climate change, and biodiversity decline. This chapter examines how bioinformatics, artificial intelligence (AI), and remote sensing transform ecological assessments, detailing techniques like metagenomics, eDNA analysis, and metabarcoding for noninvasive biodiversity tracking. AI/machine learning enhances data interpretation, predicting species distributions and ecosystem resilience, while remote sensing (hyperspectral imaging, LiDAR, synthetic aperture radar) enables real-time surveillance from microbes to landscapes. These technologies overcome traditional limitations taxonomic biases, spatial constraints, and labor intensity with applications in conservation genetics, bioremediation, disaster management, and water quality assessment. We address challenges like data standardization and computational demands, proposing an integrated framework combining molecular biology, computational science, and ecology for next-generation ecosystem stewardship.