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Acoustic noise and vibration prediction, mitigation, and performance optimization, in electric machines, are studied in this dissertation. First, vibration prediction enhancement in electric machines through frequency-dependent damping characterization is proposed in this dissertation. Different methods of mass and stiffness-dependent Rayleigh damping coefficient calculation are studied to identify the best damping estimation strategy. The proposed damping estimation strategy is used to predict the vibration spectrums of two 12-slot 10-pole (12s10p) permanent magnet synchronous machine (PMSM) designs and predicted vibration spectrums are experimentally validated through run-up tests of two prototypes. Moreover, to eliminate the dependency of the damping estimation strategy on the availability of a prototype, a damping coefficient prediction methodology is proposed. The proposed prediction methodology is experimentally validated using a third 12s10p PMSM prototype. Secondly, a lumped unit response-based sensitivity analysis procedure is introduced, which isolates electromagnetic and structural impacts brought by variation of different design parameters in an electric machine. The lumped unit response strategy utilizes the frequency-dependent damping estimation method developed early in the dissertation. The impact of different generic design parameters and a structural feature on a range of output quantities are studied in detail for a 12s10p PMSM. Analysis reveals that on a 12s10p PMSM, slot opening has a very high impact on the dominant airgap force component. A multi-level non-linear regression model-based optimization strategy is introduced considering electromagnetic and structural design objectives and constraints following the sensitivity analysis. A 12s10p PMSM prototype is tested to validate the FEA simulations used during the optimization process. Finally, the lumped unit response-based vibration prediction methodology developed in this dissertation is applied to perform a multi-speed point optimization of a targeted feature in switched reluctance machine (SRM) to mitigate acoustic noise and vibration. The targeted feature is stator pole bridges and it is applied to a 24-slot 16-pole (24s/16p) SRM. Average torque reduction due to flux shorting in stator pole bridges is addressed by utilizing a material with low magnetic permeability for the bridges. Multi-physics aspects and manufacturing issues of stator pole bridge design are presented in the chapter. The enhanced vibration prediction methodology proposed in this dissertation is used to perform a wide speed range optimization of stator pole bridge thickness. Following the optimization, two 100-kW SRM prototypes are built. Experimental results verify simulation-based predictions and report a maximum noise reduction of 12.52 dBA in the stator pole bridge model compared to the baseline SRM with no bridges.