  A celestial alignment between Neptune, Uranus, and Jupiter will occur in the
early 2030s, allowing a slingshot around Jupiter to gain enough momentum to
achieve planetary flyover capability around the two ice giants. The launch of
the uranian probe for the departure windows of the NUIP mission is between
January 2030 and January 2035, and the duration of the mission is between six
and ten years, and the launch of the Nepta probe for the departure windows of
the NUIP mission is between February 2031 and April 2032 and the duration of
the mission is between seven and ten years. To get the most out of alignment,
deep learning methods are expected to play a critical role in autonomous and
intelligent spatial guidance problems. This would reduce travel time, hence
mission time, and allow the spacecraft to perform well for the life of its
sophisticated instruments and power systems up to fifteen years. This article
proposes a design of deep neural networks, namely convolutional neural networks
and recurrent neural networks, capable of predicting optimal control actions
and image classification during the mission. Nepta-Uranian interplanetary
mission, using only raw images taken by optimal onboard cameras. It also
describes the unique requirements and constraints of the NUIP mission, which
led to the design of the communications system for the Nepta-Uranian
spacecraft. The proposed mission is expected to collect telemetry data on
Uranus and Neptune while performing the flyovers and transmit the obtained data
to Earth for further analysis. The advanced range of spectrometers and particle
detectors available would allow better quantification of the ice giant's
properties.
