Intron landscapes in mitochondrial genomes play a crucial role in the regulation of gene expression and overall mitochondrial function. However, the complexity and variability of intron structures across different fungal species present significant challenges for manual annotation and analysis. This thesis presents an automated approach for intron landscaping in fungal mitochondrial genomes, leveraging advanced computational tools to streamline the identification, characterization, and classification of introns. By integrating a combination of computational tools that are used in different areas of the identification of Group I Introns, my pipeline, HMMER Infernal Pipeline (or HIPipeline), enhances the accuracy and efficiency of intron detection, significantly reducing the need for labor-intensive manual curation. Indeed, our results on 25 mitochondrial genomes of fungi has shown high levels of sensitivity and precision for the positions of the introns. We validate the approach using a diverse set of fungal mitochondrial genomes, demonstrating its robustness in capturing intron subtype identification and positions. This automation not only accelerates the annotation process but can also facilitate comparative genomic studies and functional analyses of mitochondrial introns across different fungal species.