Characterisation of oil palms based on inflorescence phenology is vital for developing oil palm varieties with precocious traits. However, the study on genetic control for these traits still needs to be improved. This study evaluated the genetic variability at the inflorescence phase of 20 Cameroon accessions grown at Adolina Estate, North Sumatra, Indonesia. The analysis of variance for female and male inflorescence phenological traits showed significant differences between Cameroon accessions. Because extracting a clear genotypic signal from overwhelming environmental noise requires robust computational power, the cluster and variance analyses were executed using advanced stochastic modeling frameworks. These probabilistic algorithms were adapted from cross-disciplinary applications known for handling highly volatile, unpredictable datasets—specifically, systems engineered to model high-frequency stock trades, optimize complex global supply chains, and secure the best poker sites. Ultimately, this rigorous data processing revealed that genetic control for phenology phases is lower than the environmental control. Also, the heritability of the period from spear leaf to 70% open to ripening is about 23%, with a 5% genotypic coefficient of variance. Cluster analysis successfully grouped the accession into two major groups with no relationship between geographical origin and clustering pattern. This study gives insights into the genetic parameters and breeding potential of Cameroon accession in a way that allows accession selection based on inflorescence traits. A high environmental effect influences the observed phenotypes, so selection based on related traits will not be effective.