The study aimed to assess the impact of gross loan portfolio on the value of Kenyan commercial banks. Contextually, it prioritized banks listed at NSE. It set out specific variables such as; real estate loans, retail and personal, enterprise and trade loans, and agricultural loans. The assessment provided a diligent inquiry into research questions, bridging the research gap. Notably, the period of study spanned from 2017-2021. In addition, the population entailed eleven registered listed commercial banks. Panel data on listed commercial banks were collected across five years to investigate the connection between gross loan portfolio and bank value. The study was executed with the assistance and reliance on secondary cross-sectional panel data design. Data was obtained from the respective listed kenyan banks annual and published statements, historical data from the Nairobi securities exchange as well as Central Bank. Quantitative techniques were employed in data analysis; collected data was tabulated and presented to make easy to understand and interpret. SPSS statistical analysis software was used in this research to manipulate the data and visualize the results. This ANOVA p-value of 0.05 shows that the model was statistically significant. The regression analysis showed a 56.4% correlation among the variables that were under study however, only retail and personal loans alongside trade and enterprise loans had a significant relationship with bank value. The model summary also shows that 31.8% variation in banks' value was caused by agricultural , Real estate , enterprise and trade loans and retail and personal loans. It is worth emphasizing that real estate loans, retail and personal loans, agricultural loans, and enterprise and trade loans played a pivotal role in the bank's value. The evaluation of the loan diversification portfolio recommends strengthening of policies that increase the performance of loans and sealing loopholes that create avenues for defaults. Stressing on the need for a diversification strategy to avoid the negative association of bad loans on value of the bank. Finally, in a replicate study addition of other predictor variables, moderating and intervening variables can spearhead the conclusive findings.

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