This　study　explores　the　performance　of　Phycically-based　modelling　(PBM),　Machine　learning　(ML),　and　Hybrid　modelling　(HM)　in　soil　water　movement.　Three　types　of　models　were　tested　on　experiments　under　different　soils　and　external　pressure　head　conditions.　In　PBM,　we　proposed　an　adaptive　step-length　model　named　Time　Cellular　Automata　(TCA),　achieving　an　RMSE　of　5.91,　which　outperforms　HYDRUS　(RMSE　7.92).　In　ML,　Root　Mean　Square　Error　(RMSE)　of　all　four　tested　models　was　below　1.5,　with　eXtreme　Gradient　Boosting　(XGBoost)　performing　the　best.　The　predictive　accuracy　of　ML　significantly　outperformed　PBM.　SHapley　Additive　exPlanation　was　used　to　interpret　the　data　and　feature　importance　of　machine　learning.　Middle-layer　soil　temperature,　surface-layer　soil　salinity,　water　head　and　air　temperature　were　identified　as　important　parameters　for　ML.　Heuristic　algorithm　can　assist　in　searching　for　optimal　parameters　for　TCA　(Optimized　TCA)　and　improve　RMSE　from　5.91　to　4.79.　By　integrating　PBM　and　ML,　developed　a　hybrid　modeling　strategy　named　HM.　The　HM　was　constructed　using　XGB　and　TCA,　and　achieved　an　error　rate　falling　between　Non-Optimized　TCA　(5.91)　and　Optimized　TCA　(5.51).　This　study　presents　a　method　for　constructing　HM　from　PBM　and　ML　which　is　guided　by　data-driven　approaches　to　make　the　analysis　of　soil　water　movement　more　efficient　and　economical.