This　research　utilizes　machine　learning　methods　to　forecast　the　complex,　non-linear　thermal　phenomena,　along　with　heat　transfer　mechanisms,　that　influence　the　burning　rate　of　pool　fires,　especially　with　changes　in　ullage　height.　Experiments　involving　pool　fires　were　systematically　designed　and　carried　out,　incorporating　different　diameters　and　ullage　heights.　Heptane　was　used　as　the　representative　alkane　fuels.　A　dataset　containing　more　than　70,000　sets　of　data　was　created　as　a　training　dataset　for　training　the　Backpropagation　Neural　Network　(BPNN)　and　Support　Vector　Regression　(SVR)　models.　During　the　optimization　of　machine　learning　model　parameters,　this　study　is　based　on　Particle　Swarm　Optimization　(PSO)　with　the　principle　of　intelligent　optimization　to　efficiently　and　accurately　screen　and　optimize　the　key　parameters　of　the　model.　The　combustion　duration,　pool　dimensions,　and　non-dimensional　ullage　height　were　input　into　a　machine-learning　model　to　predict　the　burning　rate.　By　comparing　against　experimental　data,　the　model　was　found　to　be　able　to　predict　the　dynamic　evolution　of　the　burning　rate　of　the　pool　fire　in　a　real-time　manner.　The　SVR　model　demonstrates　greater　predictive　accuracy　in　comparison　to　the　BPNN　model,　and　the　relative　prediction　error　remains　within　+/-　20　%,　which　fully　proves　its　effectiveness　and　generalization　ability　in　the　prediction　of　pool　fire　burning　rate.　The　insights　gained　will　offer　substantial　scientific　backing　for　enhanced　fire　monitoring　systems,　while　highlighting　the　capability　of　advanced　machine　learning　methodologies　to　predict　the　intricate,　real-time　thermal　dynamics　and　heat　transfer　characteristics　of　burning　liquid　fuels.