This review discusses predictive biomarkers for anti-TNF alpha therapy in inflammatory bowel disease (IBD) patients. IBD is a chronic gastrointestinal condition characterized by severe gut inflammation, commonly presenting as Crohn's disease (CD) or ulcerative colitis (UC). Anti-TNF therapies, such as Infliximab and Adalimumab, have significantly improved treatment outcomes for IBD patients. However, not all patients respond well to these therapies, and some may lose response over time. The review aims to examine predictive biomarkers for monitoring therapeutic response to anti-TNF therapy in IBD patients, their limitations, and clinical utilities, to enable more personalized and effective treatment approaches. Genetic markers, such as polymorphisms in genes like FCGR3A, TLR4, TNFRSF1A, IFNG, IL6, and IL1B, have been associated with response to anti-TNF therapy. Fecal markers like calprotectin and lactoferrin are commonly used to assess disease activity but show mixed results in predicting response to anti-TNF therapy. Immune markers, such as TNF-α and IFN-γ levels, can indicate the effectiveness of anti-TNF therapy. Protein markers, including various proteins like ANG1, ANG2, CRP, CEACAM1, SAA1, EMMPRIN, TGFA, MMP1, MMP2, MMP3, MMP9, IL-6, IL-7, sCD40L, PF4, complement C4-B, apolipoprotein A-I, apolipoprotein E, serotransferrin, beta-2-glycoprotein 1, and clusterin, have been identified as potential biomarkers for predicting response to anti-TNF therapy. Microbial biomarkers, including gut microbiota composition, have also been explored as potential predictors of response to anti-TNF therapy. Anti-drug antibodies (ADAs) can affect the efficacy of anti-TNF therapy. Other factors, such as matrix metalloproteinases (MMPs), miRNAs, and environmental factors, may also influence response to anti-TNF therapy. Despite the identification of various biomarkers, their clinical utility remains limited, and further research is needed to validate their potential as predictive markers for IBD treatment. The review highlights the importance of integrating multi-omics data and clinical information to develop more effective and personalized treatment strategies for IBD patients.This review discusses predictive biomarkers for anti-TNF alpha therapy in inflammatory bowel disease (IBD) patients. IBD is a chronic gastrointestinal condition characterized by severe gut inflammation, commonly presenting as Crohn's disease (CD) or ulcerative colitis (UC). Anti-TNF therapies, such as Infliximab and Adalimumab, have significantly improved treatment outcomes for IBD patients. However, not all patients respond well to these therapies, and some may lose response over time. The review aims to examine predictive biomarkers for monitoring therapeutic response to anti-TNF therapy in IBD patients, their limitations, and clinical utilities, to enable more personalized and effective treatment approaches. Genetic markers, such as polymorphisms in genes like FCGR3A, TLR4, TNFRSF1A, IFNG, IL6, and IL1B, have been associated with response to anti-TNF therapy. Fecal markers like calprotectin and lactoferrin are commonly used to assess disease activity but show mixed results in predicting response to anti-TNF therapy. Immune markers, such as TNF-α and IFN-γ levels, can indicate the effectiveness of anti-TNF therapy. Protein markers, including various proteins like ANG1, ANG2, CRP, CEACAM1, SAA1, EMMPRIN, TGFA, MMP1, MMP2, MMP3, MMP9, IL-6, IL-7, sCD40L, PF4, complement C4-B, apolipoprotein A-I, apolipoprotein E, serotransferrin, beta-2-glycoprotein 1, and clusterin, have been identified as potential biomarkers for predicting response to anti-TNF therapy. Microbial biomarkers, including gut microbiota composition, have also been explored as potential predictors of response to anti-TNF therapy. Anti-drug antibodies (ADAs) can affect the efficacy of anti-TNF therapy. Other factors, such as matrix metalloproteinases (MMPs), miRNAs, and environmental factors, may also influence response to anti-TNF therapy. Despite the identification of various biomarkers, their clinical utility remains limited, and further research is needed to validate their potential as predictive markers for IBD treatment. The review highlights the importance of integrating multi-omics data and clinical information to develop more effective and personalized treatment strategies for IBD patients.