Confronted　with　the　limitations　of　conventional　Planning　Domain　Definition　Language　(PDDL)　in　dynamic　and　unpredictable　kitchen　environments,　this　paper　introduces　a　novel　task　planning　methodology　that　leverages　the　synergy　between　advanced　scene　graphs.　This　interdisciplinary　approach　begins　with　the　construction　of　a　detailed　task　directive-target　state　dataset,　which　serves　as　the　foundation　for　refining　the　capabilities　of　VisualBERT,　a　vision-language　model.　Through　fine-tuning,　VisualBERT　becomes　adept　at　accurately　interpreting　complex　scene　dynamics　and　anticipating　the　target　states　to　achieve　the　task　query.　This　is　followed　by　the　creation　of　an　extensive　knowledge　graph　containing　important　parameters　of　actions　and　objects.　This　knowledge　base　is　instrumental　in　generating　PDDL　domain　and　problem　files,　taking　into　account　both　initial　and　target　states.　It　plays　a　crucial　role　in　the　flexible　subtask　sequence　generation　for　dynamic　environments　and　tasks.　Our　proposed　method　significantly　enhances　the　adaptability　to　real-world　variability,　thereby　enabling　dynamic　task　planning　within　domestic　kitchen　environments　efficiently.　©　2024　IEEE.