Rhamnolipids are glycolipid biosurfactants produced by Pseudomonas aeruginosa, valued for their surface activity, biodegradability and potential as green alternatives to synthetic surfactants. The first rhamnosylation step, catalysed by RhlB, is central to the catalytic efficiency of mono-rhamnolipid formation and subsequent di-rhamnolipid biosynthesis. To explore natural diversity and enhance RhlB activity, we established a multilayer DNA–protein mining pipeline to identify polymorphisms influencing catalytic activity. The framework integrated analyses of nucleotide transitions/ transversions, mutation patterns, binding-site configurations, stability hotspots, and active-site plasticity. Phylogenetic analysis revealed two hypermutator phylogroups, highlighting evolutionary divergence within RhlB. Three rhlB variants (rhlB1, rhlB2, rhlB3) were experimentally validated in a modular rhlAB genetic construct for mono-rhamnolipid biosynthesis in Escherichia coli. Using a defined synthetic medium and high-throughput screening platform, mono-rhamnolipid production was quantified as 20.1 µg ml-1 for the reference rhlAB construct, 22.18 µg ml-1 for rhlAB1, 3.2 µg ml-1 for the dysfunctional rhlAB2 and 55.51 µg ml-1 for rhlAB3, representing a 2.76-fold increase over the reference. Functional analysis suggested that mutation H263R in RhlB2 disrupted catalytic activity, while reversion (R263H) restored activity to 24.63 µg ml-1. Enhanced catalysis in RhlB3 was attributed to loop mutations (I234V, P238R) that improved substrate binding affinity and domain alignment. This study demonstrates that integrating computational mining with experimental validation can reveal naturally optimised enzyme variants, providing a scalable framework for accelerating enzyme engineering and advancing sustainable biosurfactant production.