In 2020, Plasmodium spp the causative agent of malaria, was associated with 627,000 deaths and 241 million cases. Diagnostics play an essential role in infectious disease control and this thesis highlights how Recombinase Polymerase Amplification (RPA) could underpin the next-generation of low-cost infield diagnostics, empowering malaria eradication efforts. The thesis covers the development of a bioinformatics tool, PrimedRPA, to optimise RPA-assay design, which was subsequently validated in the detection of P. vivax, the most widespread Plasmodium parasite. The work explores adapting RPA for a one-step colorimetric assay to align diagnostic costs with existing malaria RDTs, in addition to making the assay more suitable for in-field use. Simultaneously, I outline the use of RPA in the detection of key biomarkers associated with artemisinin resistance, a critical component of existing malaria front-line therapies, through the deliberate introduction of primer-template mismatches. Building on this work, I characterise the impact of 315 primer-template mismatch combinations on RPA reaction kinetics, with the goal of developing a robust framework for RPA-based SNP genotyping. To understand the detrimental impact even a single mismatch can have upon an RPA reaction, I outline a new tool, PrimedInclusivity, which enables researchers to utilise existing whole genome sequencing surveillance data to assess assay performance in-silico, based on binding site diversity. Finally, to address the lack of whole genome sequence data for neglected Plasmodium parasites, P. ovale walkeri and P. ovale curtisi, I generate such data and develop two new and improved reference genomes, as well as perform a population genomic analysis with isolates sourced from the African continent. Overall, my thesis describes new tools for the development of RPA-based diagnostics and generation of sequence data to assist the elimination of malaria.