In many prokaryotes but limited eukaryotic species, the combination of transposon mutagenesis and high-throughput sequencing has greatly accelerated the identification of essential genes. Here we successfully applied this technique to the methylotrophic yeast Pichia pastoris and classified its conditionally essential/non-essential gene sets. Firstly, we showed that two DNA transposons, TcBuster and Sleeping beauty, had high transposition activities in P. pastoris. By merging their insertion libraries and performing Tn-seq, we identified a total of 202,858 unique insertions under glucose supported growth condition. We then developed a machine learning method to classify the 5,040 annotated genes into putatively essential, putatively non-essential, ambig1 and ambig2 groups, and validated the accuracy of this classification model. Besides, Tn-seq was also performed under methanol supported growth condition and methanol specific essential genes were identified. The comparison of conditionally essential genes between glucose and methanol supported growth conditions helped to reveal potential novel targets involved in methanol metabolism and signaling. Our findings suggest that transposon mutagenesis and Tn-seq could be applied in the methylotrophic yeast Pichia pastoris to classify conditionally essential/non-essential gene sets. Our work also shows that determining gene essentiality under different culture conditions could help to screen for novel functional components specifically involved in methanol metabolism.