In this thesis, I present a measurement of the volumetric type Ia Supernova (SN Ia) rate from the full 5-year supernova sample from the Dark Energy Survey (DES), the first scientific analysis using this sample. After applying selection criteria to the DES-SN light curves designed to achieve a relatively pure and complete sample of high-quality SN Ia events, the resulting sample comprises 2077 photometrically classified SNe Ia over the redshift range 0.1 to 0.9, the largest homogeneously selected sample in this redshift range. Based on simulations of the DES-SN sample, adjusted so that the tails of the distribution of the residuals in the supernova Hubble diagram match those of the data, I estimate the contamination of the selected sample from core-collapse SNe is 7.8% and correct the SN Ia rate accordingly. This is the first rate analysis to use only photometric supernova information, not relying on spectroscopic SN typing, spectroscopic SN redshifts, or any host information including spectroscopic and photometric redshifts. This kind of analysis is necessary to utilize the full statistical power of the DES-SN sample and will become more critical with the arrival of the Vera Rubin Observatory’s (VRO) Legacy Survey of Space and Time (LSST) and its immense supernova sample (∼ 100, 000). In order to demonstrate that this kind of analysis is viable, I validate the photometric redshift fitting and photometric classification code on simulations, using spectroscopic information that is otherwise not used in the analysis. As in previous SN Ia rate analyses (1; 2; 3), I find that the volumetric SN Ia rate is consistent with (χ^2 = 13.95 , 6 DOF) a power-law form, R(z) = k(1 + z) β/yr/Mpc^3 over the redshift range 0.1 to 0.9 and obtain k= 2.00 ± 0.21 (stat) ± 0.24 (sys) x 10^{−5} and β = 1.82 ± 0.23 (stat) ± 0.31 (sys) using only DES data and a result of k = 2.03 ± 0.19 (stat) ± 0.18 (sys) x 10^{−5} and β = 1.79 ± 0.20 (stat) ± 0.27 (sys) when adding a prior from an SDSS rate analysis to anchor the rate at low redshift. Both of these results are consistent with measurements from prior rate analyses, and they improve the statistical errors on parameters from single-survey analyses while being the first to add a systematic error analysis for their power law rate results. The analysis was performed in a blinded fashion, obscuring the exact value of the rate parameters until the analysis was finalized to prevent such consistency from being forced by analysis choices. The systematic errors in the rate parameters are estimated by varying a number of selection parameters that impact sample purity and completeness (in both the simulations and the data sample), varying the simulation core collapse model, and changing the redshift range considered in the analysis. This data sample will be used in a future analysis to measure a delay time distribution between star formation and SN Ia explosion and constrain SN Ia progenitor models.