Increasing scale of data centers and the density of server nodes pose significant challenges in producing power and energy efficient cooling infrastructures. Current fan based air cooling systems have significant inefficiencies in their operation causing oscillations in fan power consumption and temperature variations among cores. In this paper, we identify the cause these problems and propose proactive cooling mechanisms to mitigate the power peaks and temperature variations. An accurate temperature prediction model lies behind the basis of our solutions. We use a neural network-based modeling approach for predicting core temperatures of different workloads, under different core frequencies, fan speed levels, and ambient temperature. The model provides guidance for our proactive cooling mechanisms. We propose a preemptive and decoupled fan control mechanism that can remove the power peaks in fan power consumption and reduce the maximum cooling power by 53.3% on average as well as energy consumption by 22.4%. Moreover, through our decoupled fan control method and thermal-aware load balancing algorithm, we show that temperature variations in large scale platforms can be reduced from 25 C to 2 C, making cooling systems more efficient with negligible performance overhead.