The accurate estimation of helicopter component loads is an important factor in life cycle management and life extension efforts. This chapter explores continued efforts to utilize a number of computational intelligence algorithms, statistical and machine learning techniques, such as artificial neural networks, evolutionary algorithms, fuzzy sets, residual variance analysis, and others, to estimate some of these helicopter dynamic loads. For load prediction using indirect computational methods to be practical and accepted, demonstrating slight over-prediction of these loads is preferable to ensure that the impact of the actual load cycles is captured by the prediction and to incorporate a factor of safety. Subsequent calculation of the component’s fatigue life can verify the slight over-prediction of the load signal. This chapter examines a number of techniques for encouraging slight over-prediction and favoring a conservative estimate for these loads. Estimates for the main rotor normal bending on the Australian S-70-A-9 Black Hawk helicopter during a left rolling pullout at 1.5 g manoeuvre were generated from an input set consisting of thirty standard flight state and control system parameters. The results of this work show that when using a combination of these techniques, a reduction in under-prediction and increase in over-prediction can be achieved. In addition to load signal estimation, the component’s fatigue life and load exceedances can be estimated from the predicted load signal. In helicopter life cycle management, these metrics are more useful performance measures (as opposed to mean squared error or correlation of the load signal), therefore this chapter describes the process followed to calculate these measures from the load signal using Rainflow counting, material specific fatigue data (S-N curves), and damage theory. An evaluation of the proposed techniques based on the fatigue life estimates and/or load exceedances is also made.