I2 is a measure for quantifying heterogeneity in meta-analyses. It can be interpreted as the ratio of the variance between studies to the total variance in the meta-analysis. I2 is scaled from 0% to 100%, with higher values indicating more heterogeneity. As a rule of thumb: I2 >=50% may represent moderate and I2 >= 75% may represent considerable heterogeneity. The p-value corresponds to the chi2 test included in the forest plot, as explained here:

https://handbook-5-1.cochrane.org/chapter_9/9_5_2_identifying_and_measuring_heterogeneity.htm

Thus, if your calculations are correct, you don't have to worry about inconsistency between the studies in your MA.

I2 is a widely accepted measure for quantifying heterogeneity in meta-analyses, with higher values indicating increased heterogeneity. Generally, an I2 value of 50% or higher may signify moderate heterogeneity, while 75% or higher suggests considerable heterogeneity. The p-value associated with the chi2 test in the forest plot can further confirm heterogeneity, as outlined in the Cochrane Handbook.

I am worried that you are doing a meta-analysis without understanding some of the very basic concepts. You need to study the methodology you are using, otherwise you will be in danger of misusing it, and also will have difficulties in interpreting and writing up your analysis.

The heterogeneity index I2 describes the percentage of variation across studies that is due to heterogeneity rather than chance in meta-analyses (Higgins and Thompson, 2002; Higgins et al., 2003). However, I2 is not an absolute measure of heterogeneity (Borenstein et al., 2017), nor is it a measure of the strength of heterogeneity.

Furthermore, many statistical methods are available for estimating the heterogeneity variance. The heterogeneity variance given by different methods (estimators) for the same dataset can often differ significantly. Consequently, the choice of different estimators can affect the conclusion of an interlaboratory study or meta-analysis (Huang 2023). You used Medcalc that gives I2=0, meaning that the heterogeneity variance is zero. I think you might want to look into Medcalc to check which heterogeneity variance estimator is used. You may consider other estimators for comparison.

Borenstein, M., Higgins, J. P. T.,Larry, V., Hedges, L. V., & Rothstein, H. R. (2017). Basics of meta‐analysis: I2 is not an absolute measure of heterogeneity. Res Synth Methods, 8(1), 5-18. https://doi.org/10.1002/jrsm.1230.

Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539-1558. DOI: 10.1002/sim.1186. PMID: 12111919.

Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ. 327(7414), 557-560. doi: 10.1136/bmj.327.7414.557.

Huang, H. (2023). Combing estimators in interlaboratory studies and meta-analyses. Research Synthesis Methods, https://doi.org/10.1002/jrsm.1633.