SUPERSEDED VERSION: This is an early, exploratory version of this research based on empirical fitting and contained minor errors in the code. This work has been entirely superseded by a new paper that provides a full theoretical derivation. Check it out at https://zenodo.org/records/17110539This paper presents a novel computational method for approximating the integer partition function p(n) using central binomial coefficients. The approach is based on systematic analysis of the cube root ratio ∛(C(n,⌊n/2⌋)/p(n)), which reveals underlying polynomial structure amenable to empirical modeling. Key contributions include: Consistent 0.4-2% accuracy across n ∈ [4, 80,000] spanning five orders of magnitude Discovery of empirical coefficients corresponding to fundamental mathematical constants ln(2)/3, π√6/9, and 1/6 with 99.98-99.996% agreement Superior performance compared to Hardy-Ramanujan approximation, with improvement factors of 13-87× for n ≤ 70,000 (covering 99.9% of practical applications) Numerical stability and implementation simplicity using only elementary functions The work demonstrates how systematic computational exploration can reveal hidden mathematical structure in classical combinatorial problems while providing practical approximation tools with predictable error characteristics. The remarkable correspondence between fitted coefficients and established mathematical constants suggests deep theoretical relationships warranting further investigation. Keywords: integer partitions, binomial coefficients, approximation algorithms, mathematical constants, Hardy-Ramanujan formula, computational mathematics --- Files This upload contains the following files: PartitionFormsPerformanceComparison.py - Python script comparing performance of all our approximation methods against the Hardy-Ramanujan formula error_summary.csv - Comprehensive error rate comparison data across all tested ranges fig_performance_charts.png - High-resolution performance visualization chart from Figure 1 in the paper. cube-root-partition-approximation.pdf - Complete research paper with mathematical framework and results Licensing Source code (Python script): MIT License All other files): CC Attribution 4.0 International