Drug resistance in pathogens represents an escalating global health challenge, particularly in Plasmodium falciparum, the causative agent of malaria. The emergence of drug-resistant strains complicates treatment and highlights the need for rapid and accurate detection methods. In P. falciparum, resistance has been largely attributed to single nucleotide polymorphisms (SNPs) in key genes such as PfMDR1, PfKelch13, and PfCRT, which are associated with resistance to artemisinin-based combination therapies and chloroquine—two mainstays of antimalarial treatment. This study aimed to develop a bioinformatics pipeline capable of analyzing P. falciparum genomic sequences to detect and annotate SNPs that may confer drug resistance. The pipeline was implemented on the Galaxy online analysis platform using its workflow function. It processes both reference and sample sequences through alignment, mutation detection, SNP selection, and annotation based on a reference general feature format. Although no SNPs were identified directly within known drug resistance genes in the analyzed samples, the developed pipeline successfully detected and annotated SNPs across the chromosomes containing these genes. This approach provides a practical framework for future applications in point-of-care detection and surveillance of drug-resistant P. falciparum strains.